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题名	集成型手写体汉字识别方法与系统
作者	郝红卫
学位类别	工学博士
答辩日期	1996-06-01
授予单位	中国科学院自动化研究所
授予地点	中国科学院自动化研究所
导师	戴汝为
关键词	手写体汉字识别 综合集成 人机结合 集成 基于监督学习的网络集成法 Handwritten Chinese character recognition Metasynthesis Integration Network Integration based on Supervised Learning (NISL)
学位专业	模式识别与智能系统
中文摘要	脱机手写体汉字识别是一个典型的大类别数模式识别问题。汉字字符集所 具有的数量大、结构复杂和相似字多等特点，再加上手写体的变彤，使得脱机 手写体汉字识别成为字符识别领域最大的难题和最终的目标之一。 本文针对字符识别的具体问题，将从定性到定量综合集成方法的思想具体 化，提出了基于监督学习的网络集成方法，在此基础上完成了以下工作： 1 做为研究集成模型的初步尝试，首先进行了集成型自由手写体数字识别的 研究，提出了四个采用人工神经网络的自内手写体数字识别方法：为了解 决网络拒识阈值的确定问题，给出了一组公式：在此基础上，采用基于监 督学习的网络集成法，对四个单分类器进行集成。实验结果表明，网络集 成法可以大大提高系统的整体性能。 2 针对汉字量大，结构复杂等特点，将用于数字识别的网络集成法进行了改 进，提出了一种适用于大类别数模式识别问题的网络集成方法。 3 采用简单的统汁特征，构成五个汉字识别的单分类器，采用上述网络集成 法对其进行集成，得到了令人鼓舞的结果。集成后，对10套样本(3，755 类，每类10个)的识别率达到约90%，比最好的单分类器高出7.l 5％，充 分表明了该方法的有效性。 4 在windows环境下，实现了集成型手写体汉字识别系统。系统识别速度为 2.4字／秒(PC 586／90)， 5 完成了神经网络加速系统，为汉字识别和网络集成法提供了硬件支持。 总之，本文工作量巨大且富于创新，其中集成、识别一体化的思想，拓宽 了集成方法研究的思路；用于汉字识别的网络集成法的提出与实现，不仅大大 提高了手写体汉字识别的研究水平，而且为人 神经网络在大类别数模式识别 问题中的应用提供了一种切实可行的方案。 最后需要说明的是，本文的工作具有普遍意义，所提出的方法可以直接推 广到模式识别领域的其它分支。
英文摘要	Off-line handwritten Chinese character recognition is a typical large number of classes pattern recognition problem. It is considered to be very difficult and regarded as one of the ultimate goals of character recognition research due to the large vocabulary, complex structure, lots of similar characters, and infinite variations of shapes resulting from the writing style. In this dissertation, enlightened by the idea of metasynthesis from qualitative to quantitative approach, a Network Integration method based on Supervised Learning (NISL) is proposed to deal with the character recognition problems. The research work in this dissertation can be described as follows: 1. As a first step, integration methods for free handwritten numeral recognition are studied, four different classifiers used artificial neural networks are proposed. To deal with the reject threshold problem, a group of formulas are also presented. Then the results of the individual classifiers are integrated by NISL. The experimental results reveal that the system performance is greatly improved by the proposed method. 2. In order to deal with the problems of handwritten Chinese character recognition, NISL for numeral recognition is improved. Another version of NISL suitable for large vocabulary classification problems is proposed. 3. Five individual classifiers for handwritten Chinese Character recognition are proposed and their results are integrated by NISL. The experimental results are very exciting. After integration, the recognition rate for l0 sets of samples (3,755 classes, 10 samples per class) is as high as about 90%. It is higher than result of the best individual, classifier by 7.15% and fully demonstrates the effectiveness of the proposed method. 4. An integration handwritten Chinese character recognition system is implemented under Windows environment. The recognition speed is 2.4 character per second. 5. A neural network accelerating card is designed and implemented and thus provides hardware support to the system. In brief, the work in this dissertation is creative. The idea to view integration as classification provides a new way for integration research. The presentation and implementation of NISL for handwritten Chinese character recognition not only improve the Chinese character recognition research, but also provide a practical way for the application of artificial neural networks to large vocabulary classification problems. Finally, it should be pointed out that the work in this dissertation can be directly applied to the other areas of pattern recognition.
语种	中文
其他标识符	365
内容类型	学位论文
源URL	[http://ir.ia.ac.cn/handle/173211/5661]
专题	毕业生_博士学位论文
推荐引用方式 GB/T 7714	郝红卫. 集成型手写体汉字识别方法与系统[D]. 中国科学院自动化研究所. 中国科学院自动化研究所. 1996.

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