CN113705468B - Digital image recognition method based on artificial intelligence and related equipment - Google Patents
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Abstract
The invention relates to artificial intelligence and provides a digital image recognition method and related equipment based on the artificial intelligence. The method can acquire an image to be identified according to a digital image identification request, identify an image table in the image to be identified, conduct text identification on the identified image based on the image table to obtain an image text, extract a first digital text according to the image table and a key text in the image text, cut the image to be identified according to the first digital text to obtain a digital image, input the digital image into a digital identification model to obtain a second digital text, and determine the first digital text or the second digital text as a target digital text if the first digital text is identical to the second digital text. The invention can accurately identify the handwriting number in the image. Furthermore, the present invention relates to blockchain technology, wherein the target digital text can be stored in a blockchain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a digital image recognition method based on artificial intelligence and related equipment.
Background
Digital image recognition refers to recognizing digital text contained in an image. With the development of artificial intelligence, digital text in an image is currently identified mainly through OCR (optical character recognition) algorithm.
However, in some images (e.g., house business assessment images), the digital text contained in these images is often user-handwritten, as different users have different text that is handwritten to the same number, resulting in the inability of current OCR algorithms to accurately recognize handwritten numbers in the images.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a digital image recognition method and related apparatus based on artificial intelligence, which can accurately recognize handwritten numbers in an image.
In one aspect, the present invention provides an artificial intelligence-based digital image recognition method, which includes:
when a digital image identification request is received, acquiring an image to be identified according to the digital image identification request;
Identifying an image table in the image to be identified;
Performing text recognition on the image to be recognized based on the image table to obtain an image text;
extracting a first digital text from the image text according to the key text in the image table and the image text;
cutting the image to be identified according to the first digital text to obtain a digital image;
inputting the digital image into a pre-trained digital recognition model to obtain a second digital text;
And if the first digital text is the same as the second digital text, determining the first digital text or the second digital text as a target digital text.
According to a preferred embodiment of the present invention, before inputting the digital image into the pre-trained digital recognition model to obtain the second digital text, the method further comprises:
Constructing a learner, wherein the learner comprises a convolution layer, a pooling layer and a full connection layer;
acquiring a plurality of handwriting image samples, wherein each handwriting image sample comprises a sample image and handwriting text in the sample image;
Generating an image vector of the sample image according to the image pixels of the sample image;
carrying out convolution processing on the image vector based on the convolution layer to obtain a convolution characteristic;
inputting the convolution characteristics into the pooling layer to obtain text characteristics;
Acquiring a weight matrix in the full connection layer, and calculating the product of the text feature and the weight matrix to obtain a text vector;
mapping the text vector to obtain a predicted text of the sample image by the learner;
Comparing each character in the predicted text with each character in the handwritten text, and counting the different numbers of the characters in the predicted text and the handwritten text to obtain the editing distance between the predicted text and the handwritten text;
Counting the total number of characters of the predicted text to obtain a first number, and counting the total number of characters of the handwritten text to obtain a second number;
determining the numerical value with the largest value in the first quantity and the second quantity as a target quantity;
Calculating the ratio of the editing distance in the target number to obtain the recognition error rate of the learner to the sample image;
And adjusting learning parameters in the learner according to the recognition error rate until the recognition error rate is not reduced any more, so as to obtain the digital recognition model.
According to a preferred embodiment of the present invention, the acquiring the image to be identified according to the digital image identification request includes:
Analyzing the message of the digital image identification request to obtain data information carried by the message;
Acquiring a storage path and a preset label from the data information;
and acquiring an image corresponding to the preset label from the storage path as the image to be identified.
According to a preferred embodiment of the present invention, the identifying the image table in the image to be identified includes:
performing binarization processing on the image to be identified to obtain a binary image;
acquiring an image line segment in the binary image based on a preset function;
and carrying out cross processing on the image line segments according to the line segment positions of the image line segments in the binary image to obtain the image table.
According to a preferred embodiment of the present invention, the image table includes a plurality of cells, the image text includes text information and cells where the text information is located, and extracting the first digital text from the image text according to the image table and the key text in the image text includes:
Carrying out semantic analysis on the text information to obtain semantic features of the text information;
Determining the text field in which the text information is located according to the semantic features;
acquiring a plurality of preset words from a field vocabulary according to the text field, and performing word segmentation processing on the text information to obtain a plurality of text word segments;
extracting word segments which are the same as any preset word from the text word segments to serve as the key text;
Acquiring cells where the key text is located from the image text as key cells;
acquiring cells associated with the key cells from the image table as target cells;
and extracting the first digital text from the image text according to the target cell.
According to a preferred embodiment of the present invention, the cutting the image to be identified according to the first digital text, to obtain a digital image includes:
acquiring a cell where the first digital text is located from the image text as the target cell;
acquiring coordinate information of the target cell from the image to be identified;
and cutting the image to be identified according to the coordinate information to obtain a digital image.
According to a preferred embodiment of the invention, the method further comprises:
If the first digital text is different from the second digital text, generating feedback information according to the first digital text and the second digital text;
When a response result based on the feedback information is received, extracting confirmation information from the response result;
Counting the response quantity of the confirmation information which is not the second digital text;
and when the response quantity is larger than the preset quantity, adjusting model parameters of the digital identification model according to the confirmation information.
In another aspect, the present invention also provides an artificial intelligence-based digital image recognition apparatus, including:
The acquisition unit is used for acquiring an image to be identified according to the digital image identification request when the digital image identification request is received;
The identification unit is used for identifying an image table in the image to be identified;
The identification unit is further used for carrying out text identification on the image to be identified based on the image table to obtain an image text;
The extraction unit is used for extracting a first digital text from the image text according to the image table and the key text in the image text;
the cutting unit is used for cutting the image to be identified according to the first digital text to obtain a digital image;
The input unit is used for inputting the digital image into a pre-trained digital recognition model to obtain a second digital text;
And the determining unit is further used for determining the first digital text or the second digital text as a target digital text if the first digital text is the same as the second digital text.
In another aspect, the present invention also proposes an electronic device, including:
a memory storing computer readable instructions; and
And a processor executing computer readable instructions stored in the memory to implement the artificial intelligence based digital image recognition method.
In another aspect, the present invention also proposes a computer readable storage medium having stored therein computer readable instructions that are executed by a processor in an electronic device to implement the artificial intelligence based digital image recognition method.
According to the technical scheme, the first digital text can be accurately extracted from the image text through the image table and the key text, the situation that the second digital text cannot be identified by the digital identification model due to inaccurate extraction of the first digital text is avoided, so that certain convenience is provided for the digital identification model to identify the second digital text, and the identification accuracy of the target digital text can be improved by further combining comparison of the first digital text and the second digital text.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the artificial intelligence based digital image recognition method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of an artificial intelligence based digital image recognition device of the present invention.
FIG. 3 is a schematic diagram of an electronic device implementing a preferred embodiment of an artificial intelligence based digital image recognition method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a preferred embodiment of the digital image recognition method based on artificial intelligence of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The digital image recognition method based on artificial intelligence can acquire and process related data based on artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The digital image recognition method based on artificial intelligence is applied to one or more electronic devices, wherein the electronic devices are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored computer readable instructions, and the hardware comprises, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an embedded device and the like.
The electronic device may be any electronic product that can interact with a user in a human-computer manner, such as a Personal computer, a tablet computer, a smart phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.
The electronic device may comprise a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network electronic device, a group of electronic devices made up of multiple network electronic devices, or a Cloud based Cloud Computing (Cloud Computing) made up of a large number of hosts or network electronic devices.
The network on which the electronic device is located includes, but is not limited to: the internet, wide area networks, metropolitan area networks, local area networks, virtual private networks (Virtual Private Network, VPN), etc.
S11, when a digital image recognition request is received, acquiring an image to be recognized according to the digital image recognition request.
In at least one embodiment of the present invention, the digital image recognition request may be generated by a user trigger having a text recognition requirement.
The image to be identified is an image which needs to be identified by handwriting numbers. The image to be identified may be recorded with domain information in a plurality of domains.
In at least one embodiment of the present invention, the electronic device obtaining the image to be identified according to the digital image identification request includes:
Analyzing the message of the digital image identification request to obtain data information carried by the message;
Acquiring a storage path and a preset label from the data information;
and acquiring an image corresponding to the preset label from the storage path as the image to be identified.
The preset label is used for indicating that the image is not subjected to digital identification processing.
By analyzing the message, the storage path and the preset label can be quickly acquired, so that the acquisition efficiency of the image to be identified is improved, and the image to be digitally identified can be accurately acquired through the preset label.
S12, identifying an image table in the image to be identified.
In at least one embodiment of the present invention, the image table refers to a table in the image to be identified. The image table includes a plurality of cells.
In at least one embodiment of the invention, the method further comprises:
And preprocessing the image to be identified.
Noise information in the image to be identified can be removed by preprocessing the image to be identified, so that identification of the image table and the image text is improved.
In at least one embodiment of the present invention, the electronic device identifying an image table in the image to be identified includes:
performing binarization processing on the image to be identified to obtain a binary image;
acquiring an image line segment in the binary image based on a preset function;
and carrying out cross processing on the image line segments according to the line segment positions of the image line segments in the binary image to obtain the image table.
Wherein the preset function may be HoughLinesP () function.
The line segment position refers to the position of the image line segment in the binary image.
The image to be identified is subjected to binarization processing, and the median value of the binary image comprises binary information, so that the identification accuracy of the image line segments is improved, and the identification accuracy of the image table is improved.
Specifically, the electronic device analyzes the image to be identified based on a cv2.cvtcolor () function to obtain the binary image.
And S13, carrying out text recognition on the image to be recognized based on the image table to obtain an image text.
In at least one embodiment of the present invention, the image text refers to text information in the image form.
In at least one embodiment of the present invention, the electronic device performing text recognition on the image to be recognized based on the image table, and obtaining image text includes:
positioning the position of the image table from the image to be identified to obtain an image layer;
Carrying out corrosion treatment on the image layer according to a corrosion algorithm to obtain a text feature layer;
Performing expansion processing on the text feature layer by adopting a nearest neighbor search algorithm to obtain a first region;
Extracting a plurality of character forming features from the first area, and integrating the character forming features to obtain at least one line of characters;
and cutting all characters in the at least one row of characters by adopting different cutting intervals based on different characters to obtain the image text.
By positioning the image table, analysis of all pixels in the image to be identified can be avoided, the identification efficiency of the image text is improved, and by integrating the plurality of character features and cutting the at least one line of characters again, the identification accuracy of the image text can be improved.
S14, extracting a first digital text from the image text according to the image table and the key text in the image text.
In at least one embodiment of the present invention, the key text refers to a label text corresponding to a handwritten number in the image text. For example, the key text may be: and (5) adding.
The first digital text refers to handwriting numbers extracted from the image to be recognized based on an OCR algorithm.
In at least one embodiment of the present invention, the image table includes a plurality of cells, the image text includes text information and a cell where the text information is located, and the electronic device extracting, according to the image table and key text in the image text, a first digital text from the image text includes:
Carrying out semantic analysis on the text information to obtain semantic features of the text information;
Determining the text field in which the text information is located according to the semantic features;
acquiring a plurality of preset words from a field vocabulary according to the text field, and performing word segmentation processing on the text information to obtain a plurality of text word segments;
extracting word segments which are the same as any preset word from the text word segments to serve as the key text;
Acquiring cells where the key text is located from the image text as key cells;
acquiring cells associated with the key cells from the image table as target cells;
and extracting the first digital text from the image text according to the target cell.
The text field may be a house purchase contract field, etc. It will be appreciated that the text fields are different, as are the corresponding preset words. For example, if the text field is a house purchase contract field, the preset vocabulary may be: aggregate, etc., the text field is an outpatient bill, the preset vocabulary may be: total amount, etc.
By means of semantic analysis of the text information, determination accuracy of the text field can be improved, key texts in the image text can be comprehensively extracted according to comparison of the text segmentation words and the preset words, key cells where the key texts are located can be accurately determined according to mapping relations between the text information stored in the image text and the cells, and accordingly extraction accuracy of the first digital text is improved.
Specifically, the electronic device performs semantic analysis on the text information, and obtaining semantic features of the text information includes:
Acquiring MLM (Masked Language Model) network layers and NSP (Next SENTENCE PREDICATE) network layers from a network library;
Splicing the MLM network layer and the NSP network layer to obtain a semantic vector network layer;
And processing the text information by using the semantic vector network layer to obtain the semantic features.
And processing the text information through the MLM network layer and the NSP network layer to obtain semantic features with context semantic information, and improving the representation capability of the semantic features on the text information.
Specifically, the determining, by the electronic device, the text field in which the text information is located according to the semantic feature includes:
Acquiring domain features of all domains from a domain library;
calculating the feature similarity of the semantic features and the domain features;
And determining the domain corresponding to the domain feature with the minimum feature similarity as the text domain.
Through analysis of the feature similarity, the situation that the text field cannot be matched from the field library due to certain deviation of the semantic features is avoided, and accuracy of the text field is improved.
S15, cutting the image to be identified according to the first digital text to obtain a digital image.
In at least one embodiment of the invention, the digital image refers to an image slice containing the first digital text. It will be appreciated that the digital image is part of the image to be identified.
In at least one embodiment of the present invention, the electronic device cutting the image to be recognized according to the first digital text, and obtaining a digital image includes:
acquiring a cell where the first digital text is located from the image text as the target cell;
acquiring coordinate information of the target cell from the image to be identified;
and cutting the image to be identified according to the coordinate information to obtain a digital image.
The coordinate information is used for indicating the position of the target cell in the image to be identified.
Through the coordinate information corresponding to the target cell, not only the picture information contained in the number to be identified can be completely cut, but also redundant picture information can be prevented from being cut from the image to be identified, so that the influence on the identification of the second digital text is avoided.
Specifically, the electronic device cutting the image to be identified according to the coordinate information to obtain a digital image includes:
positioning a detection frame in the image to be identified according to the coordinate information;
And calling a cut () function to cut the image to be identified based on the detection frame to obtain the digital image.
S16, inputting the digital image into a pre-trained digital recognition model to obtain a second digital text.
In at least one embodiment of the invention, the number recognition model refers to a model for recognizing handwritten numbers in an image.
In at least one embodiment of the present invention, the second digital text refers to text obtained after the digital recognition model recognizes the handwritten numbers in the digital image.
In at least one embodiment of the present invention, before inputting the digital image into the pre-trained digital recognition model to obtain the second digital text, the method further comprises:
Constructing a learner, wherein the learner comprises a convolution layer, a pooling layer and a full connection layer;
acquiring a plurality of handwriting image samples, wherein each handwriting image sample comprises a sample image and handwriting text in the sample image;
Generating an image vector of the sample image according to the image pixels of the sample image;
carrying out convolution processing on the image vector based on the convolution layer to obtain a convolution characteristic;
inputting the convolution characteristics into the pooling layer to obtain text characteristics;
Acquiring a weight matrix in the full connection layer, and calculating the product of the text feature and the weight matrix to obtain a text vector;
mapping the text vector to obtain a predicted text of the sample image by the learner;
Comparing each character in the predicted text with each character in the handwritten text, and counting the different numbers of the characters in the predicted text and the handwritten text to obtain the editing distance between the predicted text and the handwritten text;
Counting the total number of characters of the predicted text to obtain a first number, and counting the total number of characters of the handwritten text to obtain a second number;
determining the numerical value with the largest value in the first quantity and the second quantity as a target quantity;
Calculating the ratio of the editing distance in the target number to obtain the recognition error rate of the learner to the sample image;
And adjusting learning parameters in the learner according to the recognition error rate until the recognition error rate is not reduced any more, so as to obtain the digital recognition model.
Wherein each handwritten image sample comprises a sample image and handwritten text in the sample image. The handwritten text in the sample image comprises a plurality of handwriting of any one of digits 0-9.
The image pixels refer to pixel values of each pixel point in the sample image.
The edit distance refers to the number of characters in the predicted text that are different from the characters in the corresponding position in the handwritten text. For example, if the predicted text is 234 and the handwritten text is 236, the edit distance is 1.
The image vector representing the sample image can be accurately generated through the image pixels, the prediction capability of the learner on the sample image can be accurately determined based on the image vector, the learning parameters are adjusted through the recognition error rate, and the recognition accuracy of the digital recognition model on the handwritten text is improved. According to the invention, the digital recognition model is generated through the plurality of handwriting images, so that the application scenes of the digital recognition model to handwriting versions of different numbers can be improved, and the recognition accuracy of the digital recognition model to the handwriting numbers is improved. Specifically, the electronic device performing mapping processing on the text vector to obtain a predicted text of the sample image by the learner includes:
Obtaining a mapping vector of each digital text in a text mapping table;
Calculating the similarity between the text vector and the mapping vector;
And determining the digital text corresponding to the mapping vector with the minimum similarity as the predicted text.
And the text mapping table stores the corresponding relation between a plurality of handwriting numbers and mapping vectors.
According to the embodiment, the prediction text corresponding to the text vector can be rapidly determined based on the text mapping table.
In at least one embodiment of the present invention, the manner in which the electronic device inputs the digital image into the digital recognition model to obtain the second digital text is similar to the manner in which the electronic device generates the predicted text of the sample image, which is not described in detail herein.
And S17, if the first digital text is the same as the second digital text, determining the first digital text or the second digital text as a target digital text.
In at least one embodiment of the present invention, the target digital text refers to identifying a handwritten number to be identified from the image to be identified.
It is emphasized that the target digital text may also be stored in a blockchain node in order to further ensure privacy and security of the target digital text.
In at least one embodiment of the invention, the method further comprises:
If the first digital text is different from the second digital text, generating feedback information according to the first digital text and the second digital text;
When a response result based on the feedback information is received, extracting confirmation information from the response result;
Counting the response quantity of the confirmation information which is not the second digital text;
and when the response quantity is larger than the preset quantity, adjusting model parameters of the digital identification model according to the confirmation information.
Wherein the response result may be a confirmation result for the feedback information.
The response number refers to the total number of second digital texts generated by the digital recognition model and the real results in the image to be recognized.
The preset number may be set according to recognition accuracy of the digital recognition model.
By the implementation manner, when the total number of the second digital texts generated by the digital recognition model and the real results in the image to be recognized is larger than the preset number, the digital recognition model can be further adjusted so as to improve the recognition accuracy of the digital recognition model on the handwritten numbers.
Specifically, the electronic device generating feedback information according to the first digital text and the second digital text includes:
Acquiring a preset message, wherein the preset message comprises a first preset label and a second preset label;
and writing the first digital text into a position corresponding to the first preset label, and writing the second digital text into a position corresponding to the second preset label to obtain the feedback information.
The first preset label is a label corresponding to the identification mode of the first digital text, and the second preset label is a label corresponding to the identification mode of the second digital text.
The feedback information can be generated rapidly through the preset message, and a user can know the specific recognition modes of the first digital text and the second digital text conveniently.
According to the technical scheme, the first digital text can be accurately extracted from the image text through the image table and the key text, the situation that the second digital text cannot be identified by the digital identification model due to inaccurate extraction of the first digital text is avoided, so that certain convenience is provided for the digital identification model to identify the second digital text, and the identification accuracy of the target digital text can be improved by further combining comparison of the first digital text and the second digital text.
FIG. 2 is a functional block diagram of a preferred embodiment of the artificial intelligence based digital image recognition device of the present invention. The artificial intelligence based digital image recognition apparatus 11 includes a generating unit 110, an acquiring unit 111, a recognition unit 112, an extracting unit 113, a cutting unit 114, an input unit 115, a determining unit 116, a preprocessing unit 117, a statistics unit 118, an adjusting unit 119, a constructing unit 120, a convolution unit 121, and a mapping unit 122. The module/unit referred to herein is a series of computer readable instructions capable of being retrieved by the processor 13 and performing a fixed function and stored in the memory 12. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
When receiving the digital image recognition request, the acquisition unit 111 acquires an image to be recognized according to the digital image recognition request.
In at least one embodiment of the present invention, the digital image recognition request may be generated by a user trigger having a text recognition requirement.
The image to be identified is an image which needs to be identified by handwriting numbers. The image to be identified may be recorded with domain information in a plurality of domains.
In at least one embodiment of the present invention, the acquiring unit 111 acquires an image to be identified according to the digital image identification request includes:
Analyzing the message of the digital image identification request to obtain data information carried by the message;
Acquiring a storage path and a preset label from the data information;
and acquiring an image corresponding to the preset label from the storage path as the image to be identified.
The preset label is used for indicating that the image is not subjected to digital identification processing.
By analyzing the message, the storage path and the preset label can be quickly acquired, so that the acquisition efficiency of the image to be identified is improved, and the image to be digitally identified can be accurately acquired through the preset label.
The identification unit 112 identifies an image table in the image to be identified.
In at least one embodiment of the present invention, the image table refers to a table in the image to be identified. The image table includes a plurality of cells.
In at least one embodiment of the present invention, the preprocessing unit 117 preprocesses the image to be recognized.
Noise information in the image to be identified can be removed by preprocessing the image to be identified, so that identification of the image table and the image text is improved.
In at least one embodiment of the present invention, the identifying unit 112 identifies an image table in the image to be identified includes:
performing binarization processing on the image to be identified to obtain a binary image;
acquiring an image line segment in the binary image based on a preset function;
and carrying out cross processing on the image line segments according to the line segment positions of the image line segments in the binary image to obtain the image table.
Wherein the preset function may be HoughLinesP () function.
The line segment position refers to the position of the image line segment in the binary image.
The image to be identified is subjected to binarization processing, and the median value of the binary image comprises binary information, so that the identification accuracy of the image line segments is improved, and the identification accuracy of the image table is improved.
Specifically, the recognition unit 112 analyzes the image to be recognized based on a cv2.cvtcolor () function, resulting in the binary image.
The recognition unit 112 performs text recognition on the image to be recognized based on the image table, and obtains an image text.
In at least one embodiment of the present invention, the image text refers to text information in the image form.
In at least one embodiment of the present invention, the identifying unit 112 performs text identification on the image to be identified based on the image table, and obtaining image text includes:
positioning the position of the image table from the image to be identified to obtain an image layer;
Carrying out corrosion treatment on the image layer according to a corrosion algorithm to obtain a text feature layer;
Performing expansion processing on the text feature layer by adopting a nearest neighbor search algorithm to obtain a first region;
Extracting a plurality of character forming features from the first area, and integrating the character forming features to obtain at least one line of characters;
and cutting all characters in the at least one row of characters by adopting different cutting intervals based on different characters to obtain the image text.
By positioning the image table, analysis of all pixels in the image to be identified can be avoided, the identification efficiency of the image text is improved, and by integrating the plurality of character features and cutting the at least one line of characters again, the identification accuracy of the image text can be improved.
The extraction unit 113 extracts a first digital text from the image text according to the image table and the key text in the image text.
In at least one embodiment of the present invention, the key text refers to a label text corresponding to a handwritten number in the image text. For example, the key text may be: and (5) adding.
The first digital text refers to handwriting numbers extracted from the image to be recognized based on an OCR algorithm.
In at least one embodiment of the present invention, the image table includes a plurality of cells, the image text includes text information and a cell where the text information is located, and the extracting unit 113 extracts a first digital text from the image text according to the image table and a key text in the image text includes:
Carrying out semantic analysis on the text information to obtain semantic features of the text information;
Determining the text field in which the text information is located according to the semantic features;
acquiring a plurality of preset words from a field vocabulary according to the text field, and performing word segmentation processing on the text information to obtain a plurality of text word segments;
extracting word segments which are the same as any preset word from the text word segments to serve as the key text;
Acquiring cells where the key text is located from the image text as key cells;
acquiring cells associated with the key cells from the image table as target cells;
and extracting the first digital text from the image text according to the target cell.
The text field may be a house purchase contract field, etc. It will be appreciated that the text fields are different, as are the corresponding preset words. For example, if the text field is a house purchase contract field, the preset vocabulary may be: aggregate, etc., the text field is an outpatient bill, the preset vocabulary may be: total amount, etc.
By means of semantic analysis of the text information, determination accuracy of the text field can be improved, key texts in the image text can be comprehensively extracted according to comparison of the text segmentation words and the preset words, key cells where the key texts are located can be accurately determined according to mapping relations between the text information stored in the image text and the cells, and accordingly extraction accuracy of the first digital text is improved.
Specifically, the extracting unit 113 performs semantic analysis on the text information, and obtaining semantic features of the text information includes:
Acquiring MLM (Masked Language Model) network layers and NSP (Next SENTENCE PREDICATE) network layers from a network library;
Splicing the MLM network layer and the NSP network layer to obtain a semantic vector network layer;
And processing the text information by using the semantic vector network layer to obtain the semantic features.
And processing the text information through the MLM network layer and the NSP network layer to obtain semantic features with context semantic information, and improving the representation capability of the semantic features on the text information.
Specifically, the determining, by the extracting unit 113, the text field in which the text information is located according to the semantic feature includes:
Acquiring domain features of all domains from a domain library;
calculating the feature similarity of the semantic features and the domain features;
And determining the domain corresponding to the domain feature with the minimum feature similarity as the text domain.
Through analysis of the feature similarity, the situation that the text field cannot be matched from the field library due to certain deviation of the semantic features is avoided, and accuracy of the text field is improved.
The cutting unit 114 cuts the image to be identified according to the first digital text, and obtains a digital image.
In at least one embodiment of the invention, the digital image refers to an image slice containing the first digital text. It will be appreciated that the digital image is part of the image to be identified.
In at least one embodiment of the present invention, the cutting unit 114 cuts the image to be recognized according to the first digital text, and obtaining the digital image includes:
acquiring a cell where the first digital text is located from the image text as the target cell;
acquiring coordinate information of the target cell from the image to be identified;
and cutting the image to be identified according to the coordinate information to obtain a digital image.
The coordinate information is used for indicating the position of the target cell in the image to be identified.
Through the coordinate information corresponding to the target cell, not only the picture information contained in the number to be identified can be completely cut, but also redundant picture information can be prevented from being cut from the image to be identified, so that the influence on the identification of the second digital text is avoided.
Specifically, the cutting unit 114 cuts the image to be identified according to the coordinate information, and the obtaining a digital image includes:
positioning a detection frame in the image to be identified according to the coordinate information;
And calling a cut () function to cut the image to be identified based on the detection frame to obtain the digital image.
The input unit 115 inputs the digital image into a pre-trained digital recognition model, resulting in a second digital text.
In at least one embodiment of the invention, the number recognition model refers to a model for recognizing handwritten numbers in an image.
In at least one embodiment of the present invention, the second digital text refers to text obtained after the digital recognition model recognizes the handwritten numbers in the digital image.
In at least one embodiment of the present invention, before inputting the digital image into a pre-trained digital recognition model to obtain a second digital text, the construction unit 120 constructs a learner, where the learner includes a convolution layer, a pooling layer, and a full connection layer;
the acquiring unit 111 acquires a plurality of handwriting image samples, each handwriting image sample including a sample image and a handwriting text in the sample image;
the generating unit 110 generates an image vector of the sample image according to the image pixels of the sample image;
the convolution unit 121 performs convolution processing on the image vector based on the convolution layer to obtain a convolution feature;
the input unit 115 inputs the convolution feature into the pooling layer to obtain a text feature;
the acquiring unit 111 acquires a weight matrix in the full connection layer, and calculates a product of the text feature and the weight matrix to obtain a text vector;
the mapping unit 122 performs mapping processing on the text vector to obtain a predicted text of the learner on the sample image;
The statistics unit 118 compares each character in the predicted text with each character in the handwritten text, and counts the number of different characters in the predicted text and the handwritten text, so as to obtain the edit distance between the predicted text and the handwritten text;
The statistics unit 118 counts the total number of characters of the predicted text to obtain a first number, and counts the total number of characters of the handwritten text to obtain a second number;
the determining unit 116 determines the value with the largest value in the first number and the second number as a target number;
The statistics unit 118 calculates the ratio of the editing distance in the target number to obtain the recognition error rate of the learner to the sample image;
the adjustment unit 119 adjusts the learning parameters in the learner according to the recognition error rate until the recognition error rate is no longer reduced, resulting in the digital recognition model.
Wherein each handwritten image sample comprises a sample image and handwritten text in the sample image. The handwritten text in the sample image comprises a plurality of handwriting of any one of digits 0-9.
The image pixels refer to pixel values of each pixel point in the sample image.
The edit distance refers to the number of characters in the predicted text that are different from the characters in the corresponding position in the handwritten text. For example, if the predicted text is 234 and the handwritten text is 236, the edit distance is 1.
The image vector representing the sample image can be accurately generated through the image pixels, the prediction capability of the learner on the sample image can be accurately determined based on the image vector, the learning parameters are adjusted through the recognition error rate, and the recognition accuracy of the digital recognition model on the handwritten text is improved. According to the invention, the digital recognition model is generated through the plurality of handwriting images, so that the application scenes of the digital recognition model to handwriting versions of different numbers can be improved, and the recognition accuracy of the digital recognition model to the handwriting numbers is improved. Specifically, the mapping unit 122 performs mapping processing on the text vector, and obtaining the predicted text of the sample image by the learner includes:
Obtaining a mapping vector of each digital text in a text mapping table;
Calculating the similarity between the text vector and the mapping vector;
And determining the digital text corresponding to the mapping vector with the minimum similarity as the predicted text.
And the text mapping table stores the corresponding relation between a plurality of handwriting numbers and mapping vectors.
According to the embodiment, the prediction text corresponding to the text vector can be rapidly determined based on the text mapping table.
In at least one embodiment of the present invention, the input unit 115 inputs the digital image into the digital recognition model to obtain the second digital text in a manner similar to that of generating the predicted text of the sample image, which will not be described in detail herein.
If the first digital text is the same as the second digital text, the determining unit 116 determines the first digital text or the second digital text as a target digital text.
In at least one embodiment of the present invention, the target digital text refers to identifying a handwritten number to be identified from the image to be identified.
It is emphasized that the target digital text may also be stored in a blockchain node in order to further ensure privacy and security of the target digital text.
In at least one embodiment of the present invention, if the first digital text is different from the second digital text, the generating unit 110 generates feedback information according to the first digital text and the second digital text;
when receiving a response result based on the feedback information, the extraction unit 113 extracts confirmation information from the response result;
The statistics unit 118 counts the number of responses in which the confirmation information is not the second digital text;
when the number of responses is greater than a preset number, the adjustment unit 119 adjusts model parameters of the digital recognition model according to the confirmation information.
Wherein the response result may be a confirmation result for the feedback information.
The response number refers to the total number of second digital texts generated by the digital recognition model and the real results in the image to be recognized.
The preset number may be set according to recognition accuracy of the digital recognition model.
By the implementation manner, when the total number of the second digital texts generated by the digital recognition model and the real results in the image to be recognized is larger than the preset number, the digital recognition model can be further adjusted so as to improve the recognition accuracy of the digital recognition model on the handwritten numbers.
Specifically, the generating unit 110 generates feedback information according to the first digital text and the second digital text includes:
Acquiring a preset message, wherein the preset message comprises a first preset label and a second preset label;
and writing the first digital text into a position corresponding to the first preset label, and writing the second digital text into a position corresponding to the second preset label to obtain the feedback information.
The first preset label is a label corresponding to the identification mode of the first digital text, and the second preset label is a label corresponding to the identification mode of the second digital text.
The feedback information can be generated rapidly through the preset message, and a user can know the specific recognition modes of the first digital text and the second digital text conveniently.
According to the technical scheme, the digital recognition model is generated through the plurality of handwriting images, the application scene of the digital recognition model to handwriting versions of different numbers can be improved, so that the recognition accuracy of the digital recognition model to handwriting numbers is improved, the first digital text can be accurately extracted from the image text through the image table and the key text, the situation that the second digital text cannot be recognized by the digital recognition model due to the fact that the first digital text is extracted inaccurately is avoided, a certain convenience is provided for recognizing the second digital text by the digital recognition model, and the recognition accuracy of the target digital text can be improved by further combining the comparison of the first digital text and the second digital text.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention for implementing an artificial intelligence-based digital image recognition method.
In one embodiment of the invention, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and computer readable instructions stored in the memory 12 and executable on the processor 13, such as an artificial intelligence based digital image recognition program.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and may include more or less components than illustrated, or may combine certain components, or different components, e.g. the electronic device 1 may further include input-output devices, network access devices, buses, etc.
The Processor 13 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor 13 is an operation core and a control center of the electronic device 1, connects various parts of the entire electronic device 1 using various interfaces and lines, and executes an operating system of the electronic device 1 and various installed applications, program codes, etc.
Illustratively, the computer readable instructions may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of computer readable instructions capable of performing a specific function, the computer readable instructions describing a process of executing the computer readable instructions in the electronic device 1. For example, the computer readable instructions may be divided into a generating unit 110, an acquiring unit 111, an identifying unit 112, an extracting unit 113, a cutting unit 114, an input unit 115, a determining unit 116, a preprocessing unit 117, a statistics unit 118, an adjusting unit 119, a constructing unit 120, a convolution unit 121, and a mapping unit 122.
The memory 12 may be used to store the computer readable instructions and/or modules, and the processor 13 may implement various functions of the electronic device 1 by executing or executing the computer readable instructions and/or modules stored in the memory 12 and invoking data stored in the memory 12. The memory 12 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the electronic device, etc. Memory 12 may include non-volatile and volatile memory, such as: a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one magnetic disk storage device, flash memory device, or other storage device.
The memory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, the memory 12 may be a physical memory, such as a memory bank, a TF card (Trans-FLASH CARD), or the like.
The integrated modules/units of the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may also be implemented by implementing all or part of the processes in the methods of the embodiments described above, by instructing the associated hardware by means of computer readable instructions, which may be stored in a computer readable storage medium, the computer readable instructions, when executed by a processor, implementing the steps of the respective method embodiments described above.
Wherein the computer readable instructions comprise computer readable instruction code which may be in the form of source code, object code, executable files, or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer readable instruction code, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory).
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
In connection with fig. 1, the memory 12 in the electronic device 1 stores computer readable instructions for implementing an artificial intelligence based digital image recognition method, the processor 13 being executable to implement:
when a digital image identification request is received, acquiring an image to be identified according to the digital image identification request;
Identifying an image table in the image to be identified;
Performing text recognition on the image to be recognized based on the image table to obtain an image text;
extracting a first digital text from the image text according to the key text in the image table and the image text;
cutting the image to be identified according to the first digital text to obtain a digital image;
inputting the digital image into a pre-trained digital recognition model to obtain a second digital text;
And if the first digital text is the same as the second digital text, determining the first digital text or the second digital text as a target digital text.
In particular, the specific implementation method of the processor 13 on the computer readable instructions may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The computer readable storage medium has stored thereon computer readable instructions, wherein the computer readable instructions when executed by the processor 13 are configured to implement the steps of:
when a digital image identification request is received, acquiring an image to be identified according to the digital image identification request;
Identifying an image table in the image to be identified;
Performing text recognition on the image to be recognized based on the image table to obtain an image text;
extracting a first digital text from the image text according to the key text in the image table and the image text;
cutting the image to be identified according to the first digital text to obtain a digital image;
inputting the digital image into a pre-trained digital recognition model to obtain a second digital text;
And if the first digital text is the same as the second digital text, determining the first digital text or the second digital text as a target digital text.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The units or means may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A digital image recognition method based on artificial intelligence, comprising:
when a digital image identification request is received, acquiring an image to be identified according to the digital image identification request;
Identifying an image table in the image to be identified;
Performing text recognition on the image to be recognized based on the image table to obtain an image text;
extracting a first digital text from the image text according to the key text in the image table and the image text;
cutting the image to be identified according to the first digital text to obtain a digital image;
inputting the digital image into a pre-trained digital recognition model to obtain a second digital text;
And if the first digital text is the same as the second digital text, determining the first digital text or the second digital text as a target digital text.
2. The artificial intelligence based digital image recognition method of claim 1, wherein prior to inputting the digital image into a pre-trained digital recognition model to obtain a second digital text, the method further comprises:
Constructing a learner, wherein the learner comprises a convolution layer, a pooling layer and a full connection layer;
acquiring a plurality of handwriting image samples, wherein each handwriting image sample comprises a sample image and handwriting text in the sample image;
Generating an image vector of the sample image according to the image pixels of the sample image;
carrying out convolution processing on the image vector based on the convolution layer to obtain a convolution characteristic;
inputting the convolution characteristics into the pooling layer to obtain text characteristics;
Acquiring a weight matrix in the full connection layer, and calculating the product of the text feature and the weight matrix to obtain a text vector;
mapping the text vector to obtain a predicted text of the sample image by the learner;
Comparing each character in the predicted text with each character in the handwritten text, and counting the different numbers of the characters in the predicted text and the handwritten text to obtain the editing distance between the predicted text and the handwritten text;
Counting the total number of characters of the predicted text to obtain a first number, and counting the total number of characters of the handwritten text to obtain a second number;
determining the numerical value with the largest value in the first quantity and the second quantity as a target quantity;
Calculating the ratio of the editing distance in the target number to obtain the recognition error rate of the learner to the sample image;
And adjusting learning parameters in the learner according to the recognition error rate until the recognition error rate is not reduced any more, so as to obtain the digital recognition model.
3. The digital image recognition method based on artificial intelligence according to claim 1, wherein the acquiring an image to be recognized according to the digital image recognition request includes:
Analyzing the message of the digital image identification request to obtain data information carried by the message;
Acquiring a storage path and a preset label from the data information;
and acquiring an image corresponding to the preset label from the storage path as the image to be identified.
4. The digital image recognition method based on artificial intelligence of claim 1, wherein the recognizing an image table in the image to be recognized includes:
performing binarization processing on the image to be identified to obtain a binary image;
acquiring an image line segment in the binary image based on a preset function;
and carrying out cross processing on the image line segments according to the line segment positions of the image line segments in the binary image to obtain the image table.
5. The method of claim 1, wherein the image table includes a plurality of cells, the image text includes text information and cells where the text information is located, and the extracting the first digital text from the image text according to the image table and the key text in the image text includes:
Carrying out semantic analysis on the text information to obtain semantic features of the text information;
Determining the text field in which the text information is located according to the semantic features;
acquiring a plurality of preset words from a field vocabulary according to the text field, and performing word segmentation processing on the text information to obtain a plurality of text word segments;
extracting word segments which are the same as any preset word from the text word segments to serve as the key text;
Acquiring cells where the key text is located from the image text as key cells;
acquiring cells associated with the key cells from the image table as target cells;
and extracting the first digital text from the image text according to the target cell.
6. The artificial intelligence based digital image recognition method of claim 5, wherein the cutting the image to be recognized from the first digital text to obtain a digital image comprises:
acquiring a cell where the first digital text is located from the image text as the target cell;
acquiring coordinate information of the target cell from the image to be identified;
and cutting the image to be identified according to the coordinate information to obtain a digital image.
7. The artificial intelligence based digital image recognition method of claim 1, further comprising:
If the first digital text is different from the second digital text, generating feedback information according to the first digital text and the second digital text;
When a response result based on the feedback information is received, extracting confirmation information from the response result;
Counting the response quantity of the confirmation information which is not the second digital text;
and when the response quantity is larger than the preset quantity, adjusting model parameters of the digital identification model according to the confirmation information.
8. A digital image recognition device based on artificial intelligence is characterized in that, the artificial intelligence-based digital image recognition apparatus includes:
The acquisition unit is used for acquiring an image to be identified according to the digital image identification request when the digital image identification request is received;
The identification unit is used for identifying an image table in the image to be identified;
The identification unit is further used for carrying out text identification on the image to be identified based on the image table to obtain an image text;
The extraction unit is used for extracting a first digital text from the image text according to the image table and the key text in the image text;
the cutting unit is used for cutting the image to be identified according to the first digital text to obtain a digital image;
The input unit is used for inputting the digital image into a pre-trained digital recognition model to obtain a second digital text;
And the determining unit is further used for determining the first digital text or the second digital text as a target digital text if the first digital text is the same as the second digital text.
9. An electronic device, the electronic device comprising:
A memory storing computer readable instructions; and
A processor executing computer readable instructions stored in the memory to implement the artificial intelligence based digital image recognition method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized by: the computer readable storage medium has stored therein computer readable instructions that are executed by a processor in an electronic device to implement the artificial intelligence based digital image recognition method of any one of claims 1 to 7.
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