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.     

人在回路的在线手写数学公式识别方法

在线手写数学公式识别面临书写字符的不确定性、数学公式结构的复杂性,以及公式书写风格因人而异等问题,特别是在公式书写中出现偶然性错误和包含复杂结构的情况下,现有的仅依赖机器的识别算法的识别准确率较低.为了解决这一问题,提出了人在回路的手写公式识别方法,该方法主要在结构分析阶段引入了人的参与,借助人对结构中歧义笔画的修改和结构补笔操作,完善和界定结构笔画和结构内笔画信息.为了评估该方法的有效性,将其与不含用户参与信息的一个基线识别方法在结构识别率和表达式识别率方面进行了对比分析.结果表明,该方法能够有效地促进用户参与到手写识别过程,同时,针对实验收集的手写数学公式数据,引入用户参与的方法能够有效地提高手写数学公式的结构和表达式识别率,分别提高了9.26％和13.99％.     

一种基于语义分析的汉语语音识别纠错方法

汉语语音识别的研究越来越重视与语言处理的结合,语音识别已经不是单纯的语音信号处理。N-gram语言模型应用到语音识别系统中,大大增强了系统的正确率和稳定性,但它也有其自身的局限性,使得语音识别出现许多语法和语义的错误结果。本文分析了语音识别产生语音和文字方面的错误的原因和类型,在概念层次网络语言模型的基础上提出了一种基于语句语义分析和混淆音矩阵的语音识别纠错方法。通过三个发音人、5万字的声音语料和216句实验语句的纠错测试,本文的纠错系统在纠正语义搭配型错误方面有比较好的表现,可克服N-gram语言模型带来的一些缺陷。本文提出的纠错方法还可以融合到语音识别系统中,以便更好地为语音识别的纠错处理服务。     

在线手写数学公式结构分析算法

在线手写数学公式输入作为一种自然、快速的数学公式输入方法有着很大的应用前景。基于识别通用数学公式结构的范畴,提出了在线手写数学公式结构识别的算法。首先定义了数学公式结构的分形、支配关系并扩展了硬约规,同时根据手写公式的特点提出了最小生成树(MST)算法中一种新的权值计算方法,在此基础之上应用最小生成树算法和统计学的方法进行公式结构分析。与其他经典算法比较,所提算法扩大了识别的结构,同时识别正确率有所提高。     

基于模糊理论的数学公式上下标关系判别

上下标关系数学公式中出现频繁又难于解决的特殊结构,容易与其它关系混淆.提出了基于模糊理论的数学公式上下标关系判别.运用模糊理论对数学公式中符号的空间区域关系进行划分,然后应用模糊识别的方法对上下标关系进行判别.实验结果表明,运用该方法能明显提高符号空间关系判别的识别率,尤其是能很好地判别手写数学公式中的空间关系,识别的正确率可达到96.4%.     

数学公式图像的结构理解与重现

数学公式图像识别与理解是文档图像处理领域的重要组成部分,目前尚无满足一般应用的处理方法. 提出了一种鲁棒的数学公式结构理解方法,使用公式图像识别结果、语法规则和句法规则分析数学公式结构,对数学公式的类型进行了完整的划分,对识别结果的错误进行自动的检查和纠正,能够自动分析数学公式符号的优先级和计算顺序. 既可以应用于数学公式图像的识别与格式转换,也可应用于对数学公式的检索和辅助编辑. 基于1 000个真实公式图像的实验结果证明了分析方法的有效性和稳定性.     

一种实时手写数学方程编辑器

该文所述的数学方程编辑器,用户可用笔和书写板输入手写的数学公式,系统通过实时字符、符号识别和图形语法分析软件产生与输入相对应的语法分析树,并由此转换成诸如Latex、数学的或LISP-like符号等输出形式,与基于特殊方程描述语言的编辑系统相比,该手写方程编辑器相对简单且使用方便。     

基于改进的N-gram模型和知识库的文本查错算法

针对语音识别引擎识别后文本容易发生散串错误和同音字错误,提出一种基于改进的N-gram模型和专业术语查错知识库的查错算法.采用Witten-Bell平滑算法解决N-gram模型训练过程中数据稀疏问题,并对N-gram模型增加权重分配,增强模型对散串错误的查错率.针对铁路特殊用语规定和同音字错误,构建一种适应关键字的专业术语查错知识库,实现知识库的自动更新.经过实验对比,该算法查错确率为87.9％,相比通用的N-gram查错模型提高52.8百分点.该算法的提出为后续的纠错以及语音识别准确率的提高奠定了基础,并对铁路车务系统语音识别技术的应用具有重要意义.     

手写数学公式的识别研究及在Android上的应用

为了开发一款在Android平板电脑上运行的手写数学公式识别的软件,首先采用特征值多级分类方法对手写字符进行识别;然后提出一种使用三叉结点结构存储数学公式的算法,将手写数学公式转换成一棵三叉树;最后利用三叉树的先序算法将三叉树转换成MathML语言即可在Web浏览器上显示。     

OOPS: a toolkit supporting mediation techniques for resolving ambiguity in recognition-based interfaces

Recognition technologies are being used extensively in both commercial and research systems. Recognizers are still error-prone however, and this results in performance problems and brittle dialogues, creating barriers to the acceptance and usefulness of recognition systems. Better interfaces to systems using recognition, which can help to reduce the burden of recognition errors, are difficult to build because of lack of knowledge about the ambiguity inherent in recognition. We present a survey of the design of correction techniques in interfaces which make use of recognizers. Based on this survey, we have created a user interface toolkit, OOPS (organized option pruning system) (Mankoff et al., Proceedings of ACM CHI 2000 Conference on Human Factors in Computing Systems, 2000, pp. 368–375). OOPS consists of a library of reusable error correction, or mediation, techniques drawn from the survey, combined with necessary architectural extensions to model and to provide structured support for ambiguity at the input event level of a GUI toolkit. The resulting infrastructure makes it easier for application developers to support error handling, thus helping to reduce the negative effects of recognition errors, and allowing us to explore new types of interfaces for dealing with ambiguity.     

A framework for improving error detection and correction in spoken dialog systems

Despite the recent improvements in performance and reliably of the different components of dialog systems, it is still crucial to devise strategies to avoid error propagation from one another. In this paper, we contribute a framework for improved error detection and correction in spoken conversational interfaces. The framework combines user behavior and error modeling to estimate the probability of the presence of errors in the user utterance. This estimation is forwarded to the dialog manager and used to compute whether it is necessary to correct possible errors. We have designed an strategy differentiating between the main misunderstanding and non-understanding scenarios, so that the dialog manager can provide an acceptable tailored response when entering the error correction state. As a proof of concept, we have applied our proposal to a customer support dialog system. Our results show the appropriateness of our technique to correctly detect and react to errors, enhancing the system performance and user satisfaction.     

A paradigm for handwriting-based intelligent tutors

This paper presents the interaction design of, and demonstration of technical feasibility for, intelligent tutoring systems that can accept handwriting input from students. Handwriting and pen input offer several affordances for students that traditional typing-based interactions do not. To illustrate these affordances, we present evidence, from tutoring mathematics, that the ability to enter problem solutions via pen input enables students to record algebraic equations more quickly, more smoothly (fewer errors), and with increased transfer to non-computer-based tasks. Furthermore our evidence shows that students tend to like pen input for these types of problems more than typing. However, a clear downside to introducing handwriting input into intelligent tutors is that the recognition of such input is not reliable. In our work, we have found that handwriting input is more likely to be useful and reliable when context is considered, for example, the context of the problem being solved. We present an intelligent tutoring system for algebra equation solving via pen-based input that is able to use context to decrease recognition errors by 18% and to reduce recognition error recovery interactions to occur on one out of every four problems. We applied user-centered design principles to reduce the negative impact of recognition errors in the following ways: (1) though students handwrite their problem-solving process, they type their final answer to reduce ambiguity for tutoring purposes, and (2) in the small number of cases in which the system must involve the student in recognition error recovery, the interaction focuses on identifying the student’s problem-solving error to keep the emphasis on tutoring. Many potential recognition errors can thus be ignored and distracting interactions are avoided. This work can inform the design of future systems for students using pen and sketch input for math or other topics by motivating the use of context and pragmatics to decrease the impact of recognition errors and put user focus on the task at hand.     

Errors and error correction in automatic speech recognition systems

Abstract

Errors, whether created by the user, the recognizer, or inadequate systems design, are an important consideration in the more widespread and successful use of automatic speech recognition (ASR). An experiment is described in which recognition errors are studied under different types of feedback. Subjects entered data verbally to a microcomputer according to four experimental conditions: namely, orthogonal combinations of spoken and visual feedback presented concurrently or terminally after six items. Although no significant differences in terms of error rates or speed of data entry were shown across the conditions, analysis of the time penalty for error correction indicated that as a general rule, there is a small timing advantage for terminal feedback, when the error rate is low. It was found that subjects do not monitor visual feedback with the same degree of accuracy as spoken, as a larger number of incorrect data entry strings was being confirmed as correct. Further evidence for the use of ‘second best’ recognition data is given, since correct recognition on re-entry could be increased from 83·0% to 92·4% when the first choice recognition was deleted from the second attempt. Finally, the implications for error correction protocols in system design are discussed.     

Detecting and Correcting Capabilities of Convolutional Codes

A convolutional code can be used to detect or correct infinite sequences of errors or to correct infinite sequences of erasures. First, erasure correction is shown to be related to error detection, as well as error detection to error correction. Next, the active burst distance is exploited, and various bounds on erasure correction, error detection, and error correction are obtained for convolutional codes. These bounds are illustrated by examples.     

光栅数显装置的非线性补偿方法研究

以提高光栅测量系统的精度为目的,提出一种基于光栅数显装置的非线性补偿方法。采用实时误差分离技术来对光栅测量误差进行修正的,通过采样点建立误差修正的数学模型,根据数学模型实现对任意测量值的误差修正。实验结果表明,该方法可有效解决由光栅本身的制造误差、光电转换部分误差及外界环境的振动、温度变化等因素带来影响光栅测量系统精度的问题,从而可大幅度地提高光栅测量系统的精度,而且这种补偿方法不但算法简单方便且经济成本较低,完全能够满足大部分光栅测量系统对于测量精度的要求。     

基于图像识别的阅卷系统的设计与实现

设计了一种阅卷系统,允许使用者使用任何颜色的圆珠笔、钢笔或铅笔在一般纸张的固定位置上涂写如"√"、"╳"、"○"等手写符号完成答题,由阅卷系统采用图像识别技术对答题卡图像进行预处理、符号识别、统计判分,实现阅卷自动化。实验表明,由于结构方面的相似性,本阅卷系统识别错误集中在符号"√"、"╳"上,而对符号"○"的识别基本无误。