CRBC-An Automated Approach for Handwriting OCR

In the realm of information management, the dig-itization of handwritten documents is pivotal. This research introduces an advanced Handwritten Optical Character Recognition (HOCR) model, leveraging Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory networks (BiL-STM), and the Connectionist Temporal Classification (CTC) loss function. Together Convolutional RNN-based Bi-LSTM CTC (CRBC), demonstrates a robust 94% accuracy, the model adapts seamlessly across various domains, presenting a scalable solution for enhanced handwritten document processing. This fusion of machine learning and natural language processing techniques contributes to improved efficiency in information management, with potential applications in diverse industries and fields.