An MQDF-CNN Hybrid Model for Offline Handwritten Chinese Character Recognition

An MQDF-CNN Hybrid Model for Offline Handwritten Chinese Character Recognition内容摘要：

2014 14th International Conference on Frontiers in Handwriting Recognition An MQDF-CNN Hybrid Model for Offline Handwritten Chinese Character Recognition Yanwei Wang, Xin Li, Changsong Liu, Xiaoqing Ding State Key Laboratory of Intelligent Technology and Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University Beijing, China {wangyw, lixin08,lcs,dxq}@ocrserv.ee.tsinghua.edu.cn Youxin Chen Beijing Samsung Telecom R&D Center Beijing, China youxin.chen@samsung.com Abstract—An MQDF-CNN hybrid model is presented for offline handwritten Chinese character recognition. The main idea behind MQDF-CNN hybrid model is that the significant difference on features and classification mechanisms between MQDF and CNN can complement each other. Linear confidence accumulation and multiplication confidence criteria are used for fusion outputs of MQDF and CNN. Experiments have been conducted on CASIA-HWDB1.1 and ICDAR2013 offline handwritten Chinese character recognition competition dataset. On both datasets, CNN beats MQDF by more than 1% of the accuracy, and the MQDF-CNN hybrid model has achieved the test accuracies of 92.03% and 94.44% respectively. The result on competition dataset is comparable to the state-of-the-art result though less training samples and only one CNN is used. Keywords-CNN; MQDF; MQDF-CNN handwritten Chinese character recognition I. bybrid model; INTRODUCTION Offline Chinese handwritten character recognition is a large scale pattern recognition problem and still remained to be unsolved. The cursive characters are written unconstrainedly and vary drastically in writing style and shape distortions. This makes the recognition exceptional difficult. The discriminative information contained in extracted features determines the upper limit of the recognition system and the classifier design explores the way to achieve good performance. Therefore, feature extraction and classifier design are the most important parts in a character recognition system. For Chinese character recognition, modified quadratic discriminant function (MQDF) [10] implemented with gradient feature [19] was commonly used for relatively higher performance with lower computation complexity. Gradient feature is well designed for most character class discrimination. However, it cannot adaptively extracted discriminative feature for each class because it is not a kind of learning based feature. Great efforts focused on classifier design, which can be divided into two categories. One is the generative model optimized with discriminative information integration. MQDF can be improved by modulating parameters directly [1][4][5] under objective functions and indirectly by sample reweighting [2][3]. MQDF assumes that the features satisfy Gaussian distribution, however features 2167-6445/14 $31.00 © 2014 IEEE DOI 10.1109/ICFHR.2014.49 of cursive character do not confirm this requirement to some extent. The difference between real data distribution and model assumption determines that MQDF cannot solve the problem thoroughly. The other kind of methods is the discriminative model, such as support vector machine (SVM) [6], and deep learning using SVM [9] etc. It models the classification boundary directly by minimizing empirical risk or structure risk without data distribution assumption. The leading performance of MQDF has changed when deep convolutional neural network (CNN) has made a break through [7][8]. Hierarchical features are discriminatively learned by CNN from classifier’s perspective and contain more discriminative information than the conventional gradient feature. However, the computation complexity of CNN is extremely high for large scale classification and training a robust CNN needs a large amount of samples. Since MQDF based method and CNN employ different features and different classification mechanisms, it’s reasonable to suppose that they will complement each other. Based on this idea, an MQDF-CNN hybrid model is proposed for offline Chinese character recognition. MQDF is implemented with gradient feature and CNN makes use of hierarchical features. To combine MQDF and CNN, two fusion criteria are evaluated. The experiments demonstrate that the results are great promising. II. FONTS SYSTEM OVERVIEW The diagram of MQDF-CNN hybrid model for offline handwritten Chinese character recognition system is given in Fig.1. The system mainly consists of three main parts, such