提高离线分类器的效率和速度,用于大字符集的在线手写识别

Ondrej Velek, M. Nakagawa
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引用次数: 1

摘要

本文提出了一种提高离线识别器在线手写识别速度和识别率的新方法。所有的训练模式根据它们的笔画数被分成几组,每组模式都有一个单独的识别器。由于单个识别器的类别数量较少,因此速度和准确性得到提高。首先,我们建立了一个识别器的模型,并表明我们的方法理论上可以将其识别速度提高到原来的45%。然后,将该方法应用于一个实际使用的离线识别器,识别率从90.73%提高到91.60%,识别时间仅为原始识别器的49.73%。我们的新方法的另一个好处是高可扩展性,因此识别器可以优化速度和大小或最佳精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing efficiency and speed of an off-line classifier employed for on-line handwriting recognition of a large character set
This paper proposes a new approach to acceleratingspeed and increasing the recognition rate of an off-linerecognizer employed for on-line handwriting recognitionof Japanese characters. All training patterns are dividedaccording their stroke number to several groups and onesingle recognizer is dedicated for each group of patterns.Since a number of categories for a single recognizer issmaller, the speed and accuracy improves. First, we makethe model of a recognizer and show that our method cantheoretically accelerate its recognition speed to 45% of theoriginal time. Then, we employ the method to a practicallyused off-line recognizer with the result that the recognitionrate is increased from 90.73% to 91.60% and therecognition time is reduced to only 49.73% of the originalone. Another benefit of our new approach is highscalability so that the recognizer can be optimized forspeed and size or for the best accuracy.
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