2023/08/29 by Yung-Hsin Chen, Chen, Yung-Hsin, Yuli Zhou +1
Computer Science · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Multimodal Machine Learning Applications #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.2308.15262
openalex publication_date 2023/08/29 · openalex created_date 2023/08/31 · openalex updated_date 2026/07/28
The study investigates the potential of post-OCR models to overcome limitations in OCR models and explores the impact of incorporating glyph embedding on post-OCR correction performance. In this study, we have developed our own post-OCR correction model. The novelty of our approach lies in embedding the OCR output using CharBERT and our unique embedding technique, capturing the visual characteristics of characters. Our findings show that post-OCR correction effectively addresses deficiencies in inferior OCR models, and glyph embedding enables the model to achieve superior results, including the ability to correct individual words.