Recognition of Handwritten Characters in Chinese Legal Amounts by Stacked Autoencoders

Handwritten Characters Recognition has long been a tough problem in pattern recognition and machine learning. Some special tasks, such as automatic check preprocessing, require Handwritten Chinese Legal Amounts recognition as a prerequisite. Since we expect to utilize machine instead of human to process bank checks, the recognition rate in such task must reach a relatively high rate. This paper proposes to use deep learning, auto-encoder as an effective approach for obtaining hierarchical representations of Isolated Handwritten Chinese Legal Amounts. Experiments show such representations are highly abstractive and can be used in character recognition. Meanwhile, a novel way by combining multiple Neural Networks in doing the work is proposed which proves to be able to improve the recognition rate significantly. This method reports a 0.64% error rate on a large test set over 10,000 samples and outperforms traditional methods using hand-crafted features and convolutional neural network committees (another deep learning model), narrowing the gap to human performance.