Handwritten Text into aWord Document Using Computer Vision and Deep Learning

This project presents a deep learning-based system for converting handwritten notes into fully editable MicrosoftWord documents. Instead of relying on traditional Convolutional Neural Networks (CNNs) and Bidirectional Long Short-TermMemory (BiLSTM) networks, this approach leverages Microsoft’s TrOCR model, a transformer-based OCR system designedfor handwritten text recognition. The use of TrOCR, pre-trained and accessible via Hugging Face, significantly enhancestranscription accuracy and reduces the complexity of training custom models. The process begins with image preprocessing torefine input quality, ensuring optimal text extraction. TrOCR’s powerful transformer architecture then deciphers handwrittentext by leveraging attention mechanisms to model contextual dependencies within sequences. Following initial transcription, apost-processing module performs spell and grammar corrections, refining the extracted text for improved readability. The finalstructured output is formatted and automatically saved as a .docx file, enabling seamless integration with document generationtools. By employing state-of-the-art transformer-based OCR, this system achieves high readability and reliability, making itsuitable for applications in education, legal documentation, healthcare records, and general document imaging. The transitionto TrOCR eliminates the need for complex recurrent architectures, ensuring a streamlined and efficient recognition pipeline.