基于Swin-UNet的破损碑刻文字识别方法OA

This paper presents a method for recognizing damaged stone inscription characters based on Swin-UNet.To accurately extract textual information from stone inscriptions,the Swin transformer ar-chitecture is employed to replace the down-sampling and up-sampling processes of the original U-Net structure in the segmentation task.The CBAM(Convoluted Basin Aggregation Module)and SENet mod-ules are integrated to optimize the feature fusion.The loss function is also refined using a weighted cross-entropy loss.Stone inscription characters in natural environments often suffer from various forms of degra-dation.Consequently,a semantic segmentation database for these characters is constructed based on real-world data sets,and an algorithm is designed to identify damaged characters by leveraging this database.Experimental results demonstrate that on real stone inscription images,the recognition accuracy reaches 32.60%for missing up to two strokes,64.20%when identifying the first five correct characters,and 77.20%for the first ten characters.Compared with other semantic segmentation models,the proposed method achieves more accurate stroke-level segmentation and yields superior performance.