An Efficient offLine Handwritten Japanese Address Recognition System Handwritten address recognition is very practical and popular in ICR (Intelligent Character Recognition) application field, especially for postal and express service. The structure of Japanese address has three components: high level, middle level and low level address. The high level and middle level address is very useful in postal applications since they indicate the destination of the express freight car. However, itu0027s not easy to achieve high recognition accuracy on handwritten address merely by general ICR engine. In this paper, an efficient handwritten Japanese address recognition system is proposed that combines general ICR engine and special processing functions. Rough recognition result is obtained from a general ICR engine based on oversegmentation strategy. Special processing for precise address correction contains three main functions: FCNbased middle level address detection and recognition, BKTreebased high level address candidates searching and DTWbased accurate high level address matching. In the experiments on 670 handwritten Japanese address images, the proposed method successfully divided the total address into three levels, and achieved stringlevel correct rates of 81.6% and 72.5% in high level and middle level address respectively.

Japanese Character Segmentation for Historical Handwritten Official Documents Using Fully Convolutional Networks This paper proposes a character segmentation method using a fully convolutional network (FCN) and a postprocessing phase. The network is trained with fivechannel images that indicate five kinds of zones within the bounding box for each characterxe2x80x94the top half, bottom half, left half, right half, and center. The postprocessing step reconstructs the bounding boxes for characters from the fivechannel image of the FCN output. The proposed method possesses the following advantages: (1) It is possible to process input images including multiple text lines directly; in other words, a text line segmentation process is unnecessary. (2) It does not rely upon character recognition. (3) It is robust to variations in the sizes of characters and the gaps between characters and also to cursive characters or character overlap. In the experiment of character segmentation, the accuracy ratio was 95% for real images of historical handwritten official documents written in Japanese.