论文标题
角色分解以解决hangul OCR中的类不平衡问题
Character decomposition to resolve class imbalance problem in Hangul OCR
论文作者
论文摘要
我们为韩国人物Hangul提供了一种新颖的OCR（光学特征识别）的方法。作为记录，Hangul可以通过描述每个字符的组合来代表11,172个不同的字符，只有52个图形。由于字符的总数可能压倒了神经网络的容量，因此现有的OCR编码方法预定了一组经常使用的字符集。这种设计选择自然会损害发行中长尾字符的性能。在这项工作中，我们证明了石墨编码不仅有效，而且是hangul OCR的性能。基准测试表明，我们的方法解决了hangul OCR的两个主要问题：类不平衡和目标班级选择。
We present a novel approach to OCR(Optical Character Recognition) of Korean character, Hangul. As a phonogram, Hangul can represent 11,172 different characters with only 52 graphemes, by describing each character with a combination of the graphemes. As the total number of the characters could overwhelm the capacity of a neural network, the existing OCR encoding methods pre-define a smaller set of characters that are frequently used. This design choice naturally compromises the performance on long-tailed characters in the distribution. In this work, we demonstrate that grapheme encoding is not only efficient but also performant for Hangul OCR. Benchmark tests show that our approach resolves two main problems of Hangul OCR: class imbalance and target class selection.
