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Handwritten numeral recognition using multi-task learning

Handwritten numeral recognition is a challenging problem due to large variation in the writing styles of different persons and high similarity in the contour of different digits. Based on the observation that the decision of scratchy/non-scratchy in the writing style could play a complementary role on the classification of handwritten numeral. In this paper, an effective multi-task learning network for handwritten numeral recognition is proposed to enhance the recognition performance. The proposed multi-task learning network consists of two tasks, which can simultaneously learn handwritten numeral recognition and the scratchy/non-scratchy decision. Furthermore, the two tasks can promote each other during training and achieve a better recognition performance. Extensive experiments on the MNIST database demonstrate that the proposed multi-task network can effectively improve the recognition accuracy and achieve a superior performance of 0.40% error rate, which outperforms most methods that take experiments on the M-NIST database.