A method for detecting and recognizing Seal instruments based on TrOCR

Seal recognition, an essential basic technology for digitizing documents, plays a key role in modern document processing. Although optical character recognition (OCR) methods based on the Transformer architecture have made breakthroughs in several fields in recent years, the recognition task for the unique scenario of curved text still faces significant technical challenges. Existing solutions generally suffer from cumbersome processes and limited accuracy, which seriously restricts the applicability of practical applications. To address the above problems, this study innovatively combines the YOLO target detection framework with the TrOCR text recognition model to propose a solution for seal content recognition. The method significantly improves text recognition accuracy in complex scenes through three key aspects: precise localization, intelligent denoising, and efficient Recognition, and it demonstrates excellent bent text adaptation capability. Experimental results show that the proposed method achieves a high accuracy of 94.8% in the bent text recognition task. This research can widely apply to sealing instrument detection and Recognition.