Automated Handwritten Text Recognition for Banking Application using OCR

The growing need for automation in the banking sector has accelerated the adoption of advanced technologies for processing high volumes of transactional data. One critical challenge is the digitization of handwritten banking documents such as cheques and application forms. Traditional Optical Character Recognition (OCR) systems often underperform with handwritten inputs due to inconsistent handwriting styles, noise, and poor scan quality. This project introduces a deep learning-based OCR system tailored for handwritten data extraction in banking documents. It employs a hybrid neural architecture combining Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) with attention mechanisms for accurate sequence prediction. The system accurately extracts essential fields—account numbers, payee names, amounts, and signatures—from handwritten cheques. To enhance recognition accuracy, preprocessing steps such as binarization, noise reduction, skew correction, and segmentation are applied. Post-processing techniques like error correction, contextual verification, and data validation ensure reliable and regulation-compliant output. The system is further optimized using standard cheque formats and field layouts to improve localization and minimize the need for large labeled datasets. Experimental results demonstrate notable improvements in accuracy, processing speed, and operational efficiency, significantly reducing manual intervention and human error. The system is scalable and adaptable, extending its use to other handwritten banking documents. In conclusion, the proposed deep learning-based OCR system offers a robust and intelligent solution for handwritten data extraction in banking, supporting the move toward paperless operations and secure digital transformation.