Design And Implementation Of Ancient Chinese Character Recognition Method Based On Tesseract-OCR

At present,the modern Chinese character recognition technology on the market has become mature.However,due to the interference of background noise and different writing styles in ancient literature,the recognition of ancient Chinese characters becomes more complex.Therefore,this paper studies and designs the recognition method of ancient Chinese characters.First of all,through the analysis of the research status of related technologies at home and abroad,the text image preprocessing methods and deep neural network are studied,and the model recognition and algorithm validation test are established.In image preprocessing,in the image preprocessing part: firstly,Matlab tool is used to design the program according to the principle of iterative method to complete the simulation experiment of image binarization;secondly,the linear gray stretching image enhancement algorithm,the quadratic function image enhancement algorithm and the tilt correction algorithm are used to calculate the distortion parameters through the coordinates of the corresponding points before and after the perspective transformation,so as to obtain the correlation before and after the transformation To restore the graphics and realize perspective transformation.Deep neural network technology: firstly,the basic principle of Tesseract OCR open source engine is introduced;secondly,the recognition algorithm of LSTM neural network is studied,including image feature extraction based on CNN and semantic information extraction based on LSTM;finally,the model structure and test results are summarized.Finally,this paper designs the ancient Chinese character recognition prototype based on Tesseract OCR,including system architecture,system function design,system prototype implementation and function test.The test results show that the design of ancient Chinese character recognition method based on Tesseract OCR can meet the actual needs in the recognition scene,and the system prototype has high market value.