Enhancing OCR Performance through Post-OCR Models: Adopting Glyph
  Embedding for Improved Correction
Yung-Hsin Chen Yuli Zhou
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.