A Holistic Recognition Approach for Woodblock-Print Mongolian Words Based on Convolutional Neural Network

This paper proposed a holistic recognition approach for woodblock-print Mongolian words using a convolutional neural network (CNN). To be specific, the whole word image is regarded as input of CNN. Hence, all the word images should be normalized into the same size before being inputted into CNN. By comparison, an appropriate normalization size has been determined in our study. Through the above manner, the woodblock-print Mongolian word images do not need to be segmented into glyphs. Thereby, the segmentation errors can be avoided under the circumstance. Furthermore, to solve the problem of imbalance distribution on our dataset, SMOTE technique is adopted to generate samples. In this way, the training procedure can be more efficient and the obtained CNN is more robust. Experimental results demonstrate that the proposed approach outperforms the segmentation based method and other baselines.