crossmodal error correction of continuous handwriting recognition by speech

In recognition-based user interface, users’ satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion algorithm supporting the crossmodal error correction. By fusing handwriting and speech recognition, the algorithm can correct errors in both character extraction and recognition of handwriting. The experimental result indicates that the algorithm is effective and efficient. Moreover, the evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.

基于数学形态学的联机手写字符识别去噪方法

手写输入时由于笔尖抖动等原因产生了大量噪声,有效地去除噪声是手写识别的前提和关键。根据联机手写识别中手写体字符形态的特性,分析了手写时由于各种原因而产生的噪声,运用数学形态学中膨胀、腐蚀、细化等基本运算,提出了一种将数学形态学应用于联机手写识别预处理的方法,该方法可以有效地消除大量的冗余信息。测试结果表明,提出的方法可行,具有很好的鲁棒性,可以配合其他方案应用于各种联机手写字符识别中。

基于多通道融合的连续手写识别纠错方法

在基于识别的界面中,用户的满意度不但由识别准确度决定,而且还受识别错误的纠正过程的影响.提出一种基于多通道融合的连续手写笔迹识别错误的纠正方法.该方法允许用户通过口述书写内容纠正手写识别中的字符提取和识别的错误.该纠错方法的核心是一种多通道融合算法.该算法通过利用语音输入约束最优手写识别结果的搜索,可纠正手写字符的切分错和识别错.实验评估结果表明,该融合算法能够有效纠正错误,计算效率高.与另外两种手写识别错误纠正方法相比,该方法具有更高的纠错效率.

基于感知的多方向在线手写笔迹文本行提取

提出一种多方向手写笔迹文本行的提取方法.该方法以视觉感知理论为基础,采取自底向上的策略,先将笔画组合成类比字符的笔画块,然后基于这些笔画块建立链接模型,最后采用分支限界搜索算法从链接模型中找出最优行排列.实验结果表明,该方法能有效地提取多方向笔迹行结构,并适用于弯曲文本行的提取.

On the Gas-Liquid Two-Phase Flow Regimes and their Recognition Methods

气液两相流体系是一个复杂的多变量随机过程体系,流型的定义、流型过渡准则和判别方法等方面的研究是多相流学科目前研究的重点内容。本文就与气液两相流流型及其判别有关的研究状况进行了回顾和评述,力图反映近年来气液两相流流型及其判别问题研究的状态和趋势。

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.