Text detection method based on text enhancement and multi-branch convolution

Because text detection technology in natural scenes is the premise of many industrial applications and the accuracy of common detection methods is not good, this paper proposes a neural network method based on text enhancement and multi-branch convolution to detect the picture text in natural scenes. Firstly, this paper adds the network structure of text area reinforcement in front of the backbone network, and increases the feature value of text area in the shallow network to strengthen the learning ability of the network to text features and suppress the expression of background features. Secondly, in view of the large difference in the aspect ratio of the scene text, this paper designs a convolution module with multi-branch structure and uses convolution kernel close to the shape of text to express the differentiated receptive field, and uses a lightweight attention mechanism to supplement the network's learning of the importance of channels with its parameters being only six times the number of channels. Finally, this paper improves the calculation formula of loss function on classification loss and detection box loss to weight text pixels and introduce the smallest rectangle covering prediction box and label box to express coincidence degree, thus improving the effectiveness of network training on text data sets. The results of ablation experiment and comparison experiment show that all the improvement measures of this method are effective, which achieves 83.3% and 82.4% F values on ICDAR2015 and MSRA-TD500 data sets, respectively, and performs well in the detection and comparison of difficult samples such as fuzzy text, light spot text and dense text.