In this paper, we propose the Glyph-Semanteme fusion Embedding (GSE) for Chinese character and apply it to Offline Handwritten Chinese Text Recognition (offline-HCTR). It is well known that the number of Chinese characters is very large and the glyphs of these characters are complex, but few researchers realize that the underlying reason for this phenomenon is that Chinese is a form of ideogram, which indicates that there are correlations between the glyph and semanteme of a character. In order to utilize this feature and create better representations for Chinese characters, firstly, we extract the glyph embedding and semanteme embedding for each Chinese character; then we propose a parameterized gated fusion strategy to automatically calculate the Glyph-Semanteme fusion Embedding for each character by fusing its glyph embedding and semanteme embedding. We apply the proposed GSE to an attention-based Encoder-decoder network for the offline-HCTR task. Furthermore, two kinds of GSE, Character-level GSE (CGSE) and Text-level GSE (TGSE), are applied to the decoder phase to yield the predictions. On the standard benchmark ICDAR-2013 HCTR competition dataset, the proposed method achieves 96.65% character-level recognition accuracy, which demonstrates the effectiveness of the proposed glyph-semanteme fusion embedding.

在本文中，我们提出了汉字字形语义融合嵌入（GSE）并将其应用于离线手写中文文本识别（offline-HCTR）。众所周知，汉字的数量非常多，这些汉字的字形很复杂，但很少有研究人员意识到这种现象的根本原因是汉字是一种表意文字，这表明汉字之间存在相关性。字符的字形和语义。为了利用这一特性，为汉字创建更好的表示，首先，我们提取每个汉字的字形嵌入和语义嵌入；然后我们提出了一种参数化门控融合策略，通过融合其字形嵌入和语义嵌入来自动计算每个字符的字形-语义融合嵌入。我们将提议的 GSE 应用于基于注意力的编码器-解码器网络，用于离线 HCTR 任务。此外，两种 GSE，字符级 GSE (CGSE) 和文本级 GSE (TGSE)，应用于解码器阶段以产生预测。在标准基准 ICDAR-2013 HCTR 竞赛数据集上，所提出的方法实现了 96.65% 的字符级识别准确率，这证明了所提出的字形-语义融合嵌入的有效性。应用于解码器阶段以产生预测。在标准基准 ICDAR-2013 HCTR 竞赛数据集上，所提出的方法实现了 96.65% 的字符级识别准确率，这证明了所提出的字形-语义融合嵌入的有效性。应用于解码器阶段以产生预测。在标准基准 ICDAR-2013 HCTR 竞赛数据集上，所提出的方法实现了 96.65% 的字符级识别准确率，这证明了所提出的字形-语义融合嵌入的有效性。