Open AccessEnd‐to‐end Tangut character database building and recognition method
Author(s) -
Ma Jinlin,
Cao Yunrui,
Ma Ziping,
Wei Lin,
Hao Chaohua
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12471
Subject(s) - softmax function , character (mathematics) , character recognition , pattern recognition (psychology) , computer science , artificial intelligence , artificial neural network , function (biology) , test data , speech recognition , image (mathematics) , mathematics , geometry , evolutionary biology , biology , programming language
Character recognition is an important research topic nowadays, and a large amount of excellent work has appeared. In contrast, research related to the recognition of Tangut characters is still in the initial stage. Creating databases and effective recognition methods that can support the recognition of Tangut characters remain a great challenge. In this paper, a labeling method based on Multi‐Model and Multi‐Prediction (MMMP) is proposed, which built a Tangut character database (TCD) and an enhanced database (called “TCD‐E”) covering 6077 classes, and five test sets were also built for specific tasks. To recognize Tangut characters effectively and quickly, a 5‐layer end‐to‐end Tangut Characters Recognition Network (TCRNet) based on CNN using shallow neural networks is designed. Its recognition accuracy on TCD‐E reaches 97.96 % $\%$ . Based on TCRNet, an end‐to‐end Similar Tangut Characters Recognition Network (STCRNet) is further proposed by improving the loss function by combining the softmax loss function with the central loss function, and its test accuracy on similar Tangut characters test set (called “TCD‐E‐S”) is 0.70 % $\%$ higher than TCRNet. Experiments show that TCD and TCD‐E can provide data support for Tangut character recognition. The recognition accuracy of TCRNet and STCRNet surpasses the previous best results.