Research On Text Detection Algorithm Based On Deep Learning Technology

With the explosion of deep learning technology,computer vision research has undergone great changes.As an important branch of computer vision research,text detection and recognition have also been affected by this wave of technological innovation.In recent years,a large number of deep learning-based research methods have emerged in this research field to improve the accuracy of related benchmark data sets.At the same time,this research also has huge commercial value in the business field of the modern era.However,some common algorithms nowadays often have their limitations,and it is difficult to achieve a balance between accuracy and speed in certain scenarios.Therefore,considering that the text detection and recognition algorithms have practical application value in the actual production environment,but most algorithms cannot be directly applied in the actual production.In response to this problem,this paper starts with two aspects:text detection algorithm and training data set,and proposes a new solution:1.Analyze the limitations of common text detection algorithms in recent years,and propose a new type of text detection algorithm,which can effectively improve the accuracy of text detection while ensuring the reasoning speed of the algorithm.2.On the other hand,in order to solve the problems of difficult text collection and small sample size for text detection and recognition with high-precision manual annotation data collection in natural document scene text recognition projects,this paper designed a synthetic data generation engine,which can be online Generate image samples with text annotation information.Based on this data generation engine,this paper produced a sample data set SynthDoc20K with 20,000 finely labeled synthetic natural scene document samples.The introduction of this synthetic data set effectively improved the accuracy of text detection and recognition algorithms in natural document scenes.