Word Recognition in Degraded Historical Documents Using Deep Neural Networks
Abstract
Document Image Analysis (DIA) converts pixel-based document images into machine-readable formats. While text recognition in printed documents generally achieves high accuracy due to their consistent structure and minimal variation, historical documents such as handwritten manuscripts and stone inscriptions present unique challenges, including script variability, skew, and degradation that may impact legibility. This study introduces a deep neural network approach to improve word recognition in degraded historical documents. By integrating decoding and deslanting methods, the proposed model achieves an accuracy of 90.8%, underscoring its effectiveness in addressing degradation and variability in historical document images.
Fields of Study & Subjects
Handwritten Text Recognition Techniques
Advanced Neural Network Applications
Generative Adversarial Networks and Image Synthesis
Publication details
- Journal / source
- Engineering Technology & Applied Science Research
- Publisher
- Engineering, Technology & Applied Science Research
- Volume
- 16
- Issue
- 1
- Pages
- 31519–31524
- Publication date
- 9 February 2026