计算机速记符教学法

本文论述了一种创新的计算机学习方法－速记符教学法,说明了速记符教学法的概念、特点和创建要求。介绍了操作型速记符号、键盘型速记符号及其它类型速记符号的结构形式。并通过对比的手法,论证了速记符教学法在电脑教材出版印刷及学习方面的优势所在。     

自动速记识别及其发展概况

本文概述了手写速记符计算机自动识别的基本内容，介绍了国内外手写速记符自动识别的现状和取得的成果，提出中文速记自动识别的研究课题、解决途径和进一步发展的方向。     

联机手写中文速记符的自动识别

手写中文速记符的自动识别是文字类识别中的一个比较特殊的课题.实现了一个联机手写中 文速记符识别系统HCSRS(handwritten Chinese shorthand recognition system),并给出了 对速记符中的基本音符、独立略符、连笔符的识别实验结果.该识别系统采用了以声符、韵 符为基元的结构识别策略.为了实现基元的有效切分,提出了一种基于切分-分析-交互结构 及其相应知识库的自调整切分算法STSA(self-tuning segmentation algorithm),从而大大 改     

手写中文速记符中音符的动态规划识别方法

本文在分析英文速记识别技术以及中文速记特点的基础上，提出了中文速记符的自动识别策略，并且以“人群速记”体系为研究对象，详细描述了用于识别速记符中297个音符的动态规划识别过程。通过采用局部平滑预处理，以及基于速记符形状特征和结构特征的粗分类措施，大大提高了动态规划识别速度和正确识别率。初步实验表明，对特定人书写的297个人群速记音符用动态规划法进行识别，正确识别率能达到93％以上。     

手写中文速记符中音箱的动态规划识别方法

本文在分析英文速记识别技术以及中文速记特点的基础上，提出了中文速记符的自动识别策略，并且以“人群速记”体系为研究对象，详细描述了用于识别速记符中２９７个音符的动态规划识别过程。通过采用局部平滑预处理，以及基于速记符形状特征和结构特征的粗分类措施，大大提高了动态规划识别速度和正确识别率，初步实验表明，对特定人书写的２９７个人群速记音符用动态规划法进行识别，正确识别率能达到９３％以上。     

电脑速记器

研究了速记技术利用电子计算机的途径，对速记键盘和速记写入板作了详细的探讨，表明在创制适合计算机处理的速记方案后，速记计算机将使速记易学易用。     

电脑速记公司——用手指追赶声音

“速记”不仅仅是一项技能，在它背后是中国巨大的速记产业市场。而“电脑速记”却赋予了这个“老职业”一个全新的容貌。     

从美国速记员资格认证看速记的重要性

美国速记员资格认证分为各个地方州认证和联邦政府认证,要求严格,规定详细,突出了速记工作的重要性和速记员职责的特别性。美国速记的历史源远流长,对中国的影响也很深远。了解和学习美国的速记工作有助于中国速记的发展,更好为现代化建设服务。     

一种机器视觉的题目符号智能识别系统设计

针对目前纠错过程中抄写错题题目的耗时、费力,且经常抄错题目等问题,提出了一种基于机器视觉技术的题目符号智能识别系统.通过VS 2010与OpenCv开发的题目各类符号识别算法,对采集到的题目符号图像进行预处理,结合题目符号的笔画特征进行一系列的数学形态学的处理,开运算和闭运算来消除不同学科不同类型的题目中的不同类型的符...     

手写中文速记识别及其发展概述

本文概述了手写中文速记计算机识别的基本内容，深入探讨了手写中文速记计算机识别的主要研究课题，并指出了进一步发展的方向。     

Online recognition of handwritten music symbols

In this paper, we propose an effective online method to recognize handwritten music symbols. Based on the fact that most music symbols can be regarded as combinations of several basic strokes, the proposed method first classifies all the strokes comprising an input symbol and then recognizes the symbol based on the results of stroke classification. For stroke classification, we propose to use three types of features, which are the size information, the histogram of directional movement angles, and the histogram of undirected movement angles. When combining classified strokes into a music symbol, we utilize their sizes and spatial relation together with their combination. The proposed method is evaluated using two datasets including HOMUS, one of the largest music symbol datasets. As a result, it achieves a significant improvements of about 10% in recognition rates compared to the state-of-the-art method for the datasets. This shows the superiority of the proposed method in online handwritten music symbol recognition.     

BREAKING AN EIGHTEENTH CENTURY SHORTHAND SYSTEM

William Coleman wrote a letter in longhand and shorthand dated 14 Jun 1796. This paper describes the cryptanalysis of the shorthand portions of the letter and the subsequent identification of the system as Byrom's Shorthand.     

Segmentation and recognition of phonetic features in handwritten Pitman shorthand

There is a wish to be able to enter text into mobile computing devices at the speed of speech. Only handwritten shorthand schemes can achieve this data recording rate. A new, overall solution to the segmentation and recognition of phonetic features in Pitman shorthand is proposed in this paper. Approaches to the recognition of consonant outlines, vowel and diphthong symbols and shortforms, which are different components of Pitman shorthand, are presented. A new rule is introduced to solve the issue of smooth junctions in the consonant outlines which was normally the bottleneck for recognition. Experiments with a set of 1127 consonant outlines, 2039 vowels and diphthongs and 841 shortforms from three shorthand writers have demonstrated that the proposed solution is quite promising. The recognition accuracies for consonant outlines, vowels and diphthongs, and shortforms achieved 75.33%, 96.86% and 91.86%, respectively. From the evaluation of 461 outlines with smooth junction, the introduction of the new rule has a great positive effect on the performance of the solution. The recognition accuracy of smooth junction improves from 37.53% to 93.41% given a writing time increase of 14.42%.     

An expert system for general symbol recognition

An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.     

Online hand-sketched figure recognition

An online algorithm capable of recognizing hand-sketched symbols such as those used in flowcharts is presented. The algorithm requires no indication of symbol segmentations and no restrictions on the stroke sequence of symbols. The algorithm has three steps: (1) candidate figure extraction for each symbol based on a graph search and distance calculation between candidate figures and reference patterns, (2) selection of the symbol sequence which minimizes the total sum of these distances, (3) connection rules application.A recognition test performed on 100 hand-sketched flowcharts and block diagrams produced a recognition rate of 96.1%.