Abstract
The proposed system for text detection using machine learning offers several benefits over traditional methods. Even in difficult circumstances, including dim illumination or complex backgrounds, the system can reliably detect text by using color conversion techniques and edge detection algorithms. The system uses Optical Character Recognition (OCR) and other machine-learning pattern recognition algorithms to increase its accuracy and enables it to handle different types of text, fonts, and languages. OCR is a tool that identifies text in images and changes it to text that computers can understand. Moreover, the system also converts text to speech and has various practical applications in fields such as assistive technology, where it can help individuals with visual impairments access text-based information. The real-time implementation of the system is useful in security and surveillance applications, where it can automatically detect and read license plates or text on signs and provide alerts to operators. Overall, the proposed system has significant practical applications and can enhance data processing and analysis in various fields.The proposed system has the potential to revolutionize text detection and recognition technology and can offer benefits in various industries and fields.
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References
Huang X, Shen T, Wang R, Gao C (2015) Text detection and recognition in natural scene images. In: 2015 International conference on estimation, detection and information fusion (ICEDIF). IEEE, pp 44–49. https://doi.org/10.1109/ICEDIF.2015.7280160
Wasankar SL, Mahajan H, Deshmukh D, Munot H (2010) Machine learning with text recognition. In: 2010 IEEE International conference on computational intelligence and computing research. IEEE, pp 1–5. https://doi.org/10.1109/ICCIC.2010.5705811
Ani R, Maria E, Joyce JJ, Sakkaravarthy V, Raja MA (2017) Smart specs: voice assisted text reading system for visually impaired persons using TTS method. In: 2017 International conference on innovations in green energy and healthcare technologies (IGEHT). IEEE, pp 1–6. https://doi.org/10.1109/IGEHT.2017.8094103
Jeeva C, Porselvi T, Krithika B, Shreya R, Priyaa GS, Sivasankari K (2022) Intelligent image text reader using easy OCR, NRCLex & NLTK. In: 2022 International conference on power, energy, control and transmission systems (ICPECTS), pp 1–6. https://doi.org/10.1109/ICPECTS56089.2022.10047136
Islam MR, Mondal C, Azam MK, Islam ASMJ (2016) Text detection and recognition using enhanced MSER detection and a novel OCR technique. In: 2016 5th International conference on informatics, electronics, and vision (ICIEV). IEEE, pp 15–20. https://doi.org/10.1109/ICIEV.2016.7760054
Padmapriya V, Archna R, Lavanya V, Sri CV (2020) A study on text recognition and obstacle detection techniques. In: 2020 International conference on system, computation, automation, and networking (ICSCAN), pp 1–6. https://doi.org/10.1109/ICSCAN49426.2020.9262368
Foundation RP (2021) Raspberry pi. https://www.raspberrypi.org/. Accessed 2022–2023
OpenCV (2022) Opencv library. https://opencv.org/. Accessed 2022–2023
Surana S, Pathak K, Gagnani M, Shrivastava V, Mahesh TR (2022) Text extraction and detection from images using machine learning techniques: a research review. In: 2022 International conference on electronics and renewable systems (ICEARS), pp 1201–1207. https://doi.org/10.1109/ICEARS53579.2022.9752274
Foundation PS (2023) Python. https://www.python.org/. Accessed 2022–2023
Zemin (2021) Gaussian blur font family. https://www.cufonfonts.com/font/gaussian-blur. Accessed 2022–2023
Ye Q, Doermann D (2015) Text detection and recognition in imagery: a survey. IEEE Transa Pattern Anal Mach Intell 37(7):1480–1500. https://doi.org/10.1109/TPAMI.2014.2366765
Rosebrock A (2021) Adaptive thresholding with OpenCV. https://pyimagesearch.com/2021/05/12/adaptive-thresholding-with-opencv-cv2-adaptivethreshold/. Accessed 2022–2023
Suriyakumar JS (2022) Python OpenCV—Morphological operations. https://www.geeksforgeeks.org/python-opencv-morphological-operations/. Accessed 2022–2023
Jagadeesan A (2022) Text detection and extraction using OpenCV and OCR. https://www.geeksforgeeks.org/text-detection-and-extraction-using-opencv-and-ocr/. Accessed 2022–2023
Naiemi F, Ghods V, Khalesi H (2022) Scene text detection and recognition: a survey. Multimed Tools Appl 81. https://doi.org/10.1007/s11042-022-12693-7
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Kumar, S., Vignesh Prabhu, P., Bhat, M.S., Kumar, S., Shubha, B. (2024). Text Detection and Recognition Using Machine Learning. In: Thirunavukkarasu, I., Kumar, R. (eds) Control and Information Sciences. CISCON 2023. Lecture Notes in Electrical Engineering, vol 1236. Springer, Singapore. https://doi.org/10.1007/978-981-97-5866-1_28
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DOI: https://doi.org/10.1007/978-981-97-5866-1_28
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