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A unified feature descriptor for generic character recognition based on zoning and histogram of gradients

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Abstract

Character recognition is one of the most interesting and challenging area of pattern recognition with several real-world application capabilities. Even though machine printed texts are recognized easily by modern day systems, still recognition of handwritten text and natural scene image text is a challenging task for these systems because of the large variation in the writing styles of the writers and uncontrolled imaging conditions respectively. Many modern day systems make use of convolutional neural networks to learn the features on their own instead of handcrafting the features. This research work deals with designing a unified handcrafted feature descriptor based on zoning and histogram of gradients which is common for all formats of printed, handwritten and natural image characters. The effectiveness of this unified feature is tested on Chars74K dataset which contains printed, handwritten and natural image characters and EMNIST dataset which contains handwritten characters. Also, the importance of extracting the outer structure of characters during skeletonization is studied in this work. The outcome shows that the recognition accuracy of the proposed feature descriptor is comparable with the state-of-art methods and even outperforms some of the modern convolutional neural network models.

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

    M. Arun & S. Arivazhagan

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  1. M. Arun
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Arun, M., Arivazhagan, S. A unified feature descriptor for generic character recognition based on zoning and histogram of gradients. Neural Comput & Applic 34, 12223–12234 (2022). https://doi.org/10.1007/s00521-022-07110-x

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