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This paper considers using deep neural networks for handwritten Chinese character recognition (HCCR) with arbitrary position, scale, and orientations. To solve this problem, we combine the recently proposed spatial transformer network (STN) with the deep residual network (DRN). The STN acts like a character shape normalization procedure. Different from the traditional heuristic shape normalization...
Deep convolutional neural networks (DCNN) have recently achieved state-of-the-art performance on handwritten Chinese character recognition (HCCR). However, most of DCNN models employ the softmax activation function and minimize cross-entropy loss, which may loss some inter-class information. To cope with this problem, we demonstrate a small but consistent advantage of using both classification and...
Writer adaptation is an important topic in handwriting recognition, which can further improve the performance of writer-independent recognizer. In this paper, we propose combining the neural network classifier with style transfer mapping (STM) for unsupervised writer adaptation, which only require writer-specific unlabeled data, and therefore is more common and efficient compared to supervised adaptation...
This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27...
Modified quadratic discriminant function (MQDF) is a state-of-the-art classifier for handwriting recognition. However, the big gap between accuracies on training and testing sets indicates that MQDF has a good capability to fit training data but the generalization performance is not promising. To solve this problem, we propose a new model called locally smoothed modified quadratic discriminant function...
Modified quadratic discriminant function (MQDF) is the state-of-the-art classifier in handwritten character recognition. Discriminative learning of MQDF can further improve its performance. Recent advances justify the efficacy of minimum classification error criteria in learning MQDF (MCE-MQDF). We provide an alternative choice to MCE-MQDF based on the Perceptron learning (PL-MQDF). For better generalization...
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