Handwritten Text Digitizer

Handwritten text recognition has become an essential application of Optical Character Recognition (OCR) due to the increasing demand for converting handwritten documents into editable digital text. Traditional OCR systems often struggle with variations in handwriting styles, noise, and image quality, resulting in reduced recognition accuracy. This paper presents Pen2Text-OCR, a deep learning-based handwritten text recognition system designed to accurately extract text from handwritten images. The proposed system employs image preprocessing techniques, including grayscale conversion, noise removal, thresholding, and segmentation, to enhance image quality before recognition. A convolutional neural network (CNN) is used for robust feature extraction, while sequence modeling and decoding techniques enable accurate character and word prediction. The recognized text is converted into an editable digital format, allowing efficient storage, searching, and document management. The system is implemented using Python, OpenCV, TensorFlow, and OCR libraries, providing an easy-to-use interface for image upload and text extraction. Experimental evaluation demonstrates that the proposed approach achieves high recognition accuracy, reduced processing time, and improved robustness compared to conventional OCR methods. The developed system can be effectively applied in educational institutions, historical document digitization, banking, healthcare, office automation, and digital archiving, where reliable handwritten text recognition is essential for efficient information processing and management.