End‐to‐end Tangut character database building and recognition method

Abstract

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.

TTN‐FCN: A Tangut character classification framework by tree tensor network and fully connected neural network

“…The datasets in this experiment include Tangut character database (TCD) and Tangut character database (TCD-E) [47]. TCD is constructed by scanning and consists of 124,624 Tangut character images with 6077 categories from the Tangut character-related documents, and the resolution of all images is 128×128.…”