海马启发式零样本学习字符识别网络
Hippocampus-heuristic Character Recognition Network for Zero-shot Learning
摘要：
由于汉字种类繁多、结构复杂，汉字识别一直是一个具有挑战性的课题。最新的研究表明，如此庞大的字符集可以分解成约500个基本汉字偏旁部首的集合，并在此基础上有效地解决了这一问题。而随着新汉字的不断出现，基本偏旁的数量也在不断扩大。目前完全依赖于现有的偏旁部首的方法对于识别这些新的汉字来说是不灵活的，并且在训练阶段不学习所有的偏旁部首的情况下无法识别这些汉字。为此，本文提出了一种新的海马启发式字符识别网络（HCRN），该网络借鉴海马的思维方式，只需训练部分偏旁部首，就能识别未见过的汉字（即零样本学习）。更具体地说，HCRN的网络架构是我们设计的一种新的伪连体网络，它可以从输入的训练字符样本对中学习特征，并使用它们来预测看不见的汉字。实验结果表明，HCRN是鲁棒的和有效的。该方法仅用500个训练汉字就能准确预测约16,330个未见过的测试汉字。HCRN的识别准确率比最先进的中文偏旁识别方法高出15%（从85.1%到99.9%），用于识别未见过的汉字。
英文摘要：
The recognition of Chinese characters has always been a challenging task due to their huge variety and complex structures. The latest research proves that such an enormous character set can be decomposed into a collection of about 500 fundamental Chinese radicals, and based on which this problem can be solved effectively. While with the constant advent of novel Chinese characters, the number of basic radicals is also expanding. The current methods that entirely rely on existing radicals are not flexible for identifying these novel characters and fail to recognize these Chinese characters without learning all of their radicals in the training stage. To this end, this paper proposes a novel Hippocampus-heuristic Character Recognition Network (HCRN), which references the way of hippocampus thinking, and can recognize unseen Chinese characters (namely zero-shot learning) only by training part of radicals. More specifically, the network architecture of HCRN is a new pseudo-siamese network designed by us, which can learn features from pairs of input training character samples and use them to predict unseen Chinese characters. The experimental results show that HCRN is robust and effective. It can accurately predict about 16,330 unseen testing Chinese characters relied on only 500 trained Chinese characters. The recognition accuracy of HCRN outperforms the state-of-the-art Chinese radical recognition approach by 15% (from 85.1% to 99.9%) for recognizing unseen Chinese characters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
