Jun Yan
ABSTRACT
In order to solve the problems that the excessive parameters of the network model in the existing handwritten character recognition methods increase the consumption of computing and storage resources, the target recognition rate is difficult to meet the real-time requirements, and it is easy to cause parameter explosion, gradient loss and over-fitting. Based on the traditional digital image processing module, a long-term and short-term memory network (LSTM) model is embedded to identify the handwritten score template of a specific test paper. This method performs normalization, noise reduction, tilt correction, and binarization preprocessing on the handwritten digital test paper image. The Hough line detection is used to cut the horizontal line, and the LSD algorithm erases the vertical line to obtain the target image. Finally, a new digital recognition algorithm is constructed based on the LSTM model. Through experimental testing and verification, the results show that the method effectively improves the rate of recognizing handwritten digit scores in pictures, reduces the number of model parameters and resource consumption, accelerates the convergence speed of the model, and meets the actual use requirements of identifying specific test paper picture templates under certain conditions.
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Index Terms
- Research on Handwritten Test Paper Score Recognition Algorithm based on LSTM model
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