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.

翻译：本研究探究了后OCR模型克服OCR模型局限性的潜力，并考察了引入字形嵌入对后OCR校正性能的影响。我们自主研发了后OCR校正模型，其创新之处在于利用CharBERT及独特的嵌入技术对OCR输出进行嵌入，从而捕获字符的视觉特征。研究结果表明，后OCR校正能有效弥补劣质OCR模型的缺陷，而字形嵌入使模型能够取得更优效果，包括能够对单个单词进行校正。