Deep Template Matching for Offline Handwritten Chinese Character Recognition
This addresses the challenge of data scarcity in offline handwritten Chinese character recognition, offering an incremental improvement for applications requiring recognition of unseen characters.
The paper tackles the problem of handwritten Chinese character recognition with limited data by proposing a siamese neural network to predict similarity between characters and templates, achieving promising generalization to new classes not in the training set on the ICDAR-2013 dataset.
Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks (CNN) provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in cases where little data is available. In this paper, we propose a novel method for learning siamese neural network which employ a special structure to predict the similarity between handwritten Chinese characters and template images. The optimization of siamese neural network can be treated as a simple binary classification problem. When the training process has been finished, the powerful discriminative features help us to generalize the predictive power not just to new data, but to entirely new classes that never appear in the training set. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method has a very promising generalization ability to the new classes that never appear in the training set.