Hindi-Handwritten-Character-Recognition using Deep learning

Abstract: Recognizing handwritten Hindi characters poses a significant challenge in the realms of machine learning and computer vision, particularly in the context of India's accelerating digitization. To address this, accurate and efficient algorithms are imperative for applications ranging from document analysis to postal automation and data entry. Leveraging the advancements in deep learning, we propose a novel approach to Hindi Handwritten Character Recognition. Our method employs a combination of Convolutional Neural Networks (CNNs) to extract image features and Recurrent Neural Networks (RNNs) to capture temporal dependencies within character sequences. Through rigorous evaluation on a standard benchmark dataset, our approach achieves state-of-the-art recognition accuracy. Furthermore, we validate its practical utility by successfully recognizing handwritten postal addresses on envelopes and other real-world applications. This research offers a promising solution to the challenges of Hindi Handwritten Character Recognition, with potential implications for advancing the digitization efforts not only in India but also in analogous regions.