Multi-font printed Chinese character recognition using multi-pooling convolutional neural network
Although previous studies have achieved effective printed Chinese character recognition (PCCR) in the case a single font or a few different fonts, large scale multi-font PCCR remains a major challenge owing to the wide variety in the shape, layout, and grey-level distribution of single Chinese characters across different font styles. This paper applies multi-pooling and data augmentation with non-linear transformation to a convolutional neural network (CNN) for multi-font PCCR. We propose a multi-pooling layer on top of the final convolutional layer; this approach is found to be robust to spatial layout variations and deformations in multi-font printed Chinese characters. Experimental results show that multi-pooling significantly improves CNN performance. In addition, we adopt a distorted sample generation technique by applying non-linear warping functions along an original font image, which distorts the local density of image-based Chinese character strokes. We find that CNN performance is further boosted by the distorted samples technique. An input character image is transformed into four distorted images and the CNN learns the original image as well as the distorted samples to classify 3755 classes (level-1 set of GB2312-80) of printed Chinese characters in 280 widely varying fonts and 120 manually selected fonts. Outstanding recognition rates of 94.38% and 99.74% are achieved in the former and latter cases, respectively, which indicates the effectiveness of the proposed methods.