Chinese Character Recognition based on Swin Transformer-Encoder

Abstract

Optical Character Recognition (OCR) technology, which converts printed or handwritten text into machine-readable text, holds significant application and research value in document digitization, information automation, and multilingual support. However, existing methods predominantly focus on English text recognition and often struggle with addressing the complexities of Chinese characters. This study proposes a Chinese text recognition model based on the Swin Transformer encoder, demonstrating its remarkable adaptability to Chinese character recognition. In the image preprocessing stage, we introduced an overlapping segmentation technique that enables the encoder to effectively capture the complex structural relationships between individual strokes in lengthy Chinese texts. Additionally, by incorporating a mapping layer between the encoder and decoder, we enhanced the Swin Transformer's adaptability to small image scenarios, thereby improving its feasibility for Chinese text recognition tasks. Experimental results indicate that this model outperforms classical models such as CRNN and ASTER on handwritten and web-based datasets, validating its robustness and reliability.