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Substroke Approach to HMM-based On-line Kanji Handwriting Recognition.

机译:基于HMM的在线汉字手写识别的替代方法。

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摘要

A new method is proposed for on-line handwriting recognition of Kanji characters. The method employs substroke HMMs as minimum units to constitute Japanese Kanji characters and utilizes the direction of pen motion. The main motivation is to fully utilize the continuous speech recognition algorithm by relating sentence speech to Kanji character, phonemes to substrokes, and grammar to Kanji structure. The proposed system consists input feature analysis, substroke HMMs, a character structure dictionary and a decoder. The present approach has the following advantages over the conventional methods that employ whole character HMMs. 1) Much smaller memory requirement for dictionary and models. 2) Fast recognition by employing efficient substroke network search. 3) Capability of recognizing characters not included in the training data if defined as a sequence of substrokes in the dictionary. 4) Capability of recognizing characters written by various different stroke orders with multiple definitions per one character in the dictionary. 5) Easiness in HMM adaptation to the user with a few sample character data.
机译:提出了一种在线识别汉字字符的新方法。该方法采用笔划HMM作为最小单位来构成日语汉字字符,并利用笔的运动方向。主要动机是通过将句子语音与汉字字符相关联,将音素与笔画相关联并将语法与汉字结构相关联来充分利用连续语音识别算法。拟议的系统包括输入特征分析,笔划HMM,字符结构字典和解码器。与采用整体特征HMM的常规方法相比,本方法具有以下优点。 1)字典和模型的内存要求要小得多。 2)通过使用有效的子笔划网络搜索进行快速识别。 3)如果定义为词典中的一系列笔划,则能够识别训练数据中未包含的字符。 4)能够识别字典中每个字符具有多个定义的各种不同笔画顺序的字符。 5)借助一些样本字符数据,可以轻松地使HMM适应用户。

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