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| Nowadays, people are increasingly using electronic formats to store data. In order to convert the table in paper into digital information, this project implements a handwritten Chinese character recognition system for table pictures, which releases the pressure of manual workload of typing handwritten Chinese character information. This project pre-processes the scanned images, extracts the handwritten text information from the images . Then segment the cell in the table and cuts the individual Chinese characters. In order to improve the accuracy of character cutting, this project adopts the combination of vertical projection and aspect ratio of character to determine the cutting position. The ResNext50 model is used for model training, and the two models are trained to recognize numbers, letters and handwritten Chinese characters respectively. The accuracy of the Chinese character recognition model is more than 90%, and that of the number and letter recognition model is 98%. Based on the contents filled in the table, the list of proper nouns is used to correct the recognition results and improve the accuracy. By calculating Levenshtein distance find the specific nouns with the highest similarity. The method proposed in this paper effectively complete the separation and recognition of handwritten Chinese characters in the table image. |
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| Keywords:Computer application; Character recognition; Deep learning; Image processing |
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