Research on Scene Text Detection and Recognition Algorithm Based on Improved MTSv2
In natural scene images,rich text content is very important for a comprehensive understanding of the scene.Aimed at the problems of complex background,sticky text,and multi-angle text in natural scene text images,a text detection and recognition algorithm based on improved MTSv2 is proposed.The detection algorithm takes MTSv2 as the base network,adopts the convolution-al block attention module(CBAM)attention mechanism to increase the weight of small text in the feature map,so as to better capture the key features in the image;the channel enhancement-feature pyramid network(CE-FPN)structure is fused to alleviate the feature aliasing problem generated by multi-scale fusion;The focal loss function is introduced to reduce the influences of the positive and neg-ative sample distribution imbalance on the recognition accuracy,making the network focused on difficult to classify the samples,and improving the generalization ability of the model.Through training on multiple text datasets and validation on the ICDAR2015 data set,the accuracy of the improved model on the scene text detection and recognition reaches 89.3％,the recall rate reaches 87.6％,and the F1 value reaches 88.5％,this model improves the above indicators to a certain extent compared with the original model.
scene texttext detectiontext recognitionCBAMCE-FPNattention mechanism
万方数据
维普
