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DIGI-Net: a deep convolutional neural network for multi-format digit recognition

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Abstract

Digitizing different formats of digits has multiple applications like door number detection, license plate detection, credit card number detection, etc. Specifically, handwritten digit recognition has gained so much popularity because of the vast applications such as recognizing ZIP codes in postal documents, amount entered in check leafs, etc. The handwritten digits are not always of the similar size, width, orientation, as they differ because of different writing styles of the persons, different writing instruments, etc. This makes the recognition of handwritten digits a tough and tricky task. The main problem occurs during the classification of the digits of similarity such as 1 and 7, 5 and 6, 3 and 8, etc. Recognizing digits from unconstrained natural images are also relatively difficult because of its large appearance variability. Printed digit recognition has been virtually solved by machine learning researchers. This work does not focus on printed digit recognition, but aims to learn the features from printed digits to recognize handwritten and natural image digits better. In this work, we are proposing DIGI-Net, a deep convolutional network, which has the ability to learn common features from three different formats (handwritten, natural images, printed font) of digits and to recognize them. The experimentation is done on MNIST, CVL single digit dataset, digits of Chars74K dataset and our proposed DIGI-Net achieved an accuracy of 99.11%, 93.29% and 97.60% respectively.

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Authors and Affiliations

  1. Centre for Image Processing and Pattern Recognition, Mepco Schlenk Engineering College, Sivakasi, 626005, India

    Arun Madakannu & Arivazhagan Selvaraj

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  1. Arun Madakannu
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  2. Arivazhagan Selvaraj
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Correspondence to Arun Madakannu.

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Madakannu, A., Selvaraj, A. DIGI-Net: a deep convolutional neural network for multi-format digit recognition. Neural Comput & Applic 32, 11373–11383 (2020). https://doi.org/10.1007/s00521-019-04632-9

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