Research On Scene Text Detection Method Based On Deep Neural Network

In recent years,scene text detection has become an important branch in the field of computer vision.However,due to the complex diversity of scene text,existing algorithms have problems of missed detection,false detection and low detection accuracy.Therefore,this paper improves the performance of scene text detection by innovating deep neural networks.The main research work and results of this paper are summarized as follows:(1)In the field of scene text detection,there are some problems such as missing small text,insufficient in precision for large text and multi-scale text boundary detection error caused by large text size fluctuation.To solve the above problems,a scene text detection network based on Learning Active Center Contour Model(LACC Model)is proposed.Firstly,the Multi-scale Feature Weight Fusion Model(MSWF Model)is constructed on the basis of residual network Res Net to extract multi-scale features and and dynamically assign weights of the input scene text images.Then the final feature fusion map is calculated to adapt to the situation where the aspect ratio of the scene text changes greatly.Finally the feature fusion map is then input into the LACC Model to predict the center point and boundary of the text box,which solves the problem of boundary detection errors caused by multi-scale text detection boxes containing too many backgrounds or partially enclosing text.The experimental results on MSRA-TD500,IC13,IC15 and IC17 MLT datasets show that this network improves the accuracy of multi-scale scene text detection.(2)For the problem that most scene text detection networks based on object detection can only detect horizontal text,which is not effective in detecting multi-orientation text,and the detection accuracy needs to be further improved,a multi-orientation scene text detection network based on clustering channel attention is proposed,the rotation angle is added to make the network have the ability to detect multi-orientation text;in the feature fusion stage,channel attention is introduced,and for the problem that existing channel attention weight calculation may introduce irrelevant channel features or omit related channel features,causing the problem of sub-optimal results,a channel attention based on clustering is prosed,the channel features are clustered to divide the clusters,and the channel weights within the clusters are calculated to achieve channel attention.The experimental results show that the proposed scene text detection network can effectively detect multi-orientation text and improve the detection accuracy.(3)In terms of system implementation,build a scene text intelligent detection and recognition system based on B/S architecture,analyze and design the function introduction,function demonstration,application scenarios,usage and other functions of scene text detection and recognition respectively,and finally adopt Layui+Springboot+Django REST Framework technical services to realize scene text detection and embed the algorithm of this research topic into the actual application system.