Text recognition is one of the important fields of computer vision and is widely used in automated office, assisted reading and other fields. With the continuous development of deep learning, the visual language text recognition method generated by the combination of optical character recognition and natural language processing involves the technology of recognizing text information from images or videos, which greatly improves the machine's understanding ability and interaction efficiency. However, for some niche fonts, such as Urdu, Xixia, and Xiaozhuan, these characters have complex structures, diverse strokes, and sloppy writing, making them a challenging field in text recognition. Taking Small Seal Scripts, also called Xiaozhuan, as an example, this paper summarizes the advantages and disadvantages of the current Xiaozhuan text recognition algorithm, and proposes algorithm improvement suggestions for the segmentation of Xiaozhuan characters on seals. Xiaozhuan fonts are quite different from modern Chinese characters, mainly reflected in the stroke structure, writing style, and the correlation between characters. It has smoother lines and many glyphs are unique. This complexity makes it difficult to directly apply traditional Optical Character Recognition (OCR) technology. Therefore, many studies have combined image preprocessing, character segmentation, and deep learning models for automatic recognition. Future research should focus more on the generalization and lightweight of the model so that it can run on devices with limited computing resources.
