Detection and Recognition Algorithms for Chinese and English Scene Text Images
The complex background of scene text images makes it challenging for detection algorithms to locate text regions accu-rately,leading to difficulties in recognition.To simultaneously detect and recognize scene text content in both Chinese and Eng-lish languages,and improve the accuracy of detection and recognition,an improved algorithmic model TD-ABCNetv2 based on ABCNetv2 network is proposed.Addressing the issue of variations in text features such as shape,arrangement,and font,this model adopts SKNet as the backbone network and introduces the Selective Kernel module to help the network learn features of dif-ferent scales,accommodating texts of various scales,shapes,and orientations.Considering the different character sizes and in-tervals of Chinese and English scene texts,the ECA attention module is added to the FPN structure to integrate the channel infor-mation more effectively,enhance the network's sensitivity to different features,and make the feature fusion more targeted.Addi-tionally,the CIoU loss function is introduced to more accurately measure the degree of overlap between bounding boxes,adapt to changes in the shape of the text,and enhance the generalization ability of the model.The experimental results show the proposed model is validated through experiments on several public datasets.
scene textChinese text detectionSKNetattention mechanismIoU
万方数据
维普
