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.

Text Line Segmentation in Historical Document Images Using an Adaptive UNet Architecture On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learningbased methods has been noted for solving various subfields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learningbased method for text line segmentation of historical documents. The proposed method is based on using an adaptive UNet architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the stateoftheart methods.