Dawei Liang1,2,4, Meng Wu1,*, Yan Hu3
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 279-293, 2023, DOI:10.32604/cmc.2023.035279
Abstract As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and… More >
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