Hindi-Handwritten-Character- Recognition using Deep Learning

Le résumé fourni par la source

Abstract: Hindi-Handwritten-Character- Recognition is animportant problem in the field of machine learning andcomputer vision. With the increasing digitization of India, there is a growing need to develop accurate and efficient algorithms for recognizing handwritten Hindi characters, which can be used in a variety of applications such as document analysis, postal automation, and data entry. In recent years, deep learning has emerged as a powerful tool for solving complex recognition problems. In this work, we propose a deep learning-based approach to the Hindi-Handwritten Character-Recognition. Specifically, we use a convolutional neural network (CNN) to extract features from the input images, and are current neural network (RNN) to model the temporal dependencies in the sequence of characters. Our approach is evaluated on a benchmark dataset of handwritten Hindi characters, achieving state-of- the-art results in terms of recognition accuracy. We also demonstrate the effectiveness of our approach on real-worldapplications, such as recognizing handwritten postal addresses on envelopes. Overall, our work provides a promising solution to the problem of Hindi-Hand-written- Character-Recognition, which can havea significant impact on the digitization of India and other similar regions.

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