LeNet-5 handwritten digit recognition based on deep learning

Mingliang Lu; Na Ke

doi:10.1117/12.3049259

12 November 2024 LeNet-5 handwritten digit recognition based on deep learning

Mingliang Lu, Na Ke

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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133953T (2024) https://doi.org/10.1117/12.3049259
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China

Abstract

Handwritten digit recognition is a typical application of computer vision, and its results can be widely used in the fields of zip code recognition, statistical report recognition, and test score determination. Handwritten digit recognition is still a hotspot in image recognition and classification, and the deep learning algorithm based on convolutional neural network (CNN) has the structural characteristics of local region connection, weight sharing, and down sampling, which makes convolutional neural network have an excellent performance in the field of image processing. In the paper, the adaptive binarization method is used to realize the segmentation of handwritten digits and background, the individual digits are segmented and extracted sequentially using the improved algorithm based on directional projection, the LeNet-5 model of convolutional neural network is trained by the handwritten Minist training dataset, and the segmentation and recognition of multiple handwritten digits within a single image is realized using TensorFlow. The experimental results show that the method in the paper has high reliability, and the average recognition rate of the trained model for new handwritten digits is above 92%, which achieves the expected results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Mingliang Lu and Na Ke "LeNet-5 handwritten digit recognition based on deep learning", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133953T (12 November 2024); https://doi.org/10.1117/12.3049259

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