Hand Written Text Recognition Using Deep Learning

This is the authors' abstract. We don't add key points for this paper.

Due to the wide variation in handwriting styles, writing tools, and image quality. Traditional OCR methods often struggle with cursive or free-form handwriting. This research presents a deep learning-based solution that combines Convolutional Neural Networks (CNNs) for extracting features, Bidirectional LSTMs for understanding text sequences, and CTC loss to align predictions without needing pre-segmented data. Our system is tested on standard datasets like IAM and other different models, supports recognition in multiple languages, including English, Hindi, and Hinglish. The results show improved accuracy compared to traditional methods. This approach is well-suited for real-world applications such as digitizing handwritten documents, processing forms, and creating assistive tools. Future work will focus on enabling real-time use and integrating the system into accessible applications