Automated Extraction of Handwritten Text from Forms Using Advanced Handwritten Text Recognition (HTR)

This study introduces an advanced Handwritten Text Recognition (HTR) system designed to extract and organize handwritten data from form images accurately. The system presents a modular architecture that routes input through field-specific recognition models and integrates layout-aware preprocessing with structured output generation, enabling seamless extraction from complex forms. Leveraging deep learning techniques, the system employs neural network architectures trained on diverse datasets to enhance recognition performance across varied handwriting styles and document formats. The process encompasses comprehensive image preprocessing-including noise reduction, normalization, and segmentation-to improve data quality before recognition. The core recognition component utilizes sequence-to-sequence models with attention mechanisms, enabling effective decoding of complex handwritten inputs, including mixed alphanumeric characters and cursive styles. Experimental evaluation demonstrates that the proposed system outperforms traditional OCR approaches, achieving higher accuracy and robustness in practical scenarios such as ID verification, form automation, and record digitization. The results highlight the system’s potential to reduce manual transcription efforts, minimize errors, and streamline data processing workflows across multiple applications. This research addresses current limitations in HTR technology, particularly recognizing freeform, unconstrained handwriting, and aims to facilitate scalable deployment in real-world environments.