截面表示法柱平面图的自动识别方法

针对截面表示法柱平面图的规则和特征,从轮廓追踪、全局联系、图元特征、语义分析等技术层面,分别对柱平面图的图元识别、图元与标注匹配、截面模板复制匹配、标注识别理解等几个关键步骤的处理进行了探讨,并提出了相应的算法。     

分割图形粘连字符的图元屏蔽技术研究

在工程图纸计算机处理过程中,分割字符与图形是非常重要的步骤,但字符与图形粘连的问题很难处理。本文在分析了几种字符与图形分割技术的原理和实现方法的基础上,提出一种分割与图形粘连字符的方法——图元屏蔽技术。试验结果表明,这种方法对于分割与正交方向线段粘连的字符十分有效,特别适用于处理字符串中全部与图形粘连或多个粘连的现象。     

蒙古语固定短语识别算法的设计与实现

固定短语的自动识别和标注是进行蒙古语文本处理的基础和前提条件。词类标注、短语标注、句法分析、语义分类及语义角色标注等基础研究和机器翻译、文本校对等应用系统的开发均以正确标注固定短语的文本为处理对象。该文在“蒙古语固定短语语法信息词典”的基础上采用基于有限状态自动机和规则的方法设计实现了固定短语识别和标注算法。经实验,其识别率已达到90%以上,在处理中,词均用时与基于字符串匹配的算法相比提高较多,达到0.005 0ms。     

工程图标注字符的提取与识别

本文介绍了一种基于连通域检测的工程图标注字符的提取与识别方法；首先建立了一种基于连通域和子连通域的工程图的层次表示，然后利用大小判据从连通域中找出字符候选，再利用共线判据提取出标注字符串，最后在预先总结出来的字符串模板的指导下，对提取出的字符串进行分析和识别。     

基于BCH编码的局部化标注水印算法

本文提出一种基于BCH编码和小波变换的局部化标注数字水印算法.本算法为公开数字水印算法,提取水印时不需要原图的任何信息.算法利用图像中稳定的特征点标示水印嵌入的位置,在每个特征点对应的局部区域中,独立嵌入预先用BCH容错编码的标注信息(字符串).当我们只得到部分图像时仍能通过这些特征点来定位并提取水印,读出嵌入的字符串.实验表明,算法对裁剪有很强的抵抗能力,同时对压缩、噪声等也有必要的鲁棒性.此水印算法在不要求准确提取所嵌入的字符信息时,还可以作为鲁棒水印使用;在能保证数据无失真时,则可以嵌入大量标注信息.     

基于不变矩和神经网络的常用电气符号自动识别方法研究

工程图纸中电气符号的自动识别是实现电气工程图纸矢量化的基础。提出了提取工程图纸中电气符号的不变特征。把不变特征值输入神经网络进行训练，得到神经网络模型识别旋转、平移、缩放电气元件图元的方法。实验结果表明：所建立的网络模型具有较好的性能。将此方法用于工程图纸的矢量化处理过程，只需识别出电力工程图纸中各个符号的类型，避免了图纸矢量化过程中的跟踪和拟合的过程，因此优化了矢量化流程。     

图元文件及其操作

图元文件不同于GIF,BMP、TIFF等文件以像素为最小单位保存图像,而是以图元(图元是像素的集合,如线段,圆弧,正方形,字符串等等)形式保存图像。就其实质来说,图元文件保存的是产生目标图像的GDI命令的集合,Windows通过调用这些GDI命令来产生目标图像。由于图元文件保存的是产生目标图像的方法而不是目标图像本身,因而图元文件同矢量图一样,可以进行无级放大、缩小,也可以进行内容检索。这些特点使图元文件在数据压缩方面有广阔的用途。下面介绍图元文件的结构特点及操作技巧。     

工程图纸图像图文自动分割工具SegChar

文章分析了工程图纸图像图文分割的技术特点、关键步骤和基本框架,着重介绍了图文自动分割工具SegChar采用的技术,如:(1) 自动字符尺寸阈值过滤技术,可使图文分割过程自动化和智能化;(2) 任意方向、任意长度字符串检测技术,通过精确HOUGH空间需求、松弛共线、基于字符串的HOUGH域更新等策略,提高了字符分割的处理速度,降低了处理的空间复杂度,能够使复杂的中西文字符串得以完整提取.文章最后给出了性能评价.     

基于直线段跟踪的手绘图元离线识别方法*

手绘图形是人类思维外化和表达意图的一种有效方式,如何有效地提取手绘在图纸上的图形元素是理解绘图者意图的关键问题。鉴于手绘图形是由基本图元组合构成,采用层次结构逐步实现图元提取的思想,提出了一种手绘基本图元（线段、弧、圆和椭圆）的离线识别方法。在提取图形笔画骨架像素的基础上,跟踪骨架像素得到图形的直线段描述;通过对直线段序列的分析,进行直线段序列的断开和连接处理,形成图元的曲线段描述,通过对图元曲线段描述的分析得出图元的几何参数。实验表明,该方法能够以高精确度快速识别出图像中包含的手绘图元,具有良好的稳定性     

地图及工程图纸文字标注的提取

文字标注的提取和识别对地图和工程图纸的输入自动化具有重要的意义，本文依据大比例城市地图运用改进的ＩＳＯＤＡＴＡ方法和人工智能技术，实现了文字标注的提取，具有速度快、适应性强、准确度高的特点，减少了人工编辑工作量，提高了地图输入效率，本方法适用于工程图纸的文字标注的提取。     

基于自适应尺度边缘特征的建筑施工图重叠字符识别方法研究

目前非重叠字符的识别技术已趋于完善,但难以识别建筑工程图纸标注等场景中的重叠字符,阻碍了基于二维扫描图纸的自动建模技术的突破。针对传统字符识别方法无法识别重叠字符的现状,提出了一套基于自适应尺度边缘特征的建筑施工图重叠字符识别新方法。基于像素空间分布特征初步确定重叠字符区域,定义并提取字符的自适应尺度边缘特征;借助双变量匹配概率函数筛选“位置+内容”的结果组合,并以全局最优原则代替绝对阈值作为识别标准,最终输出正确的识别结果。不同于先修复后识别的常规思路,该方法将特征匹配与干扰过滤相结合、字符定位与字符识别相关联,能解决百度等成熟商用OCR无法解决的重叠字符识别问题,且经数据实验证实具备较高的识别准确率。     

Automatic analysis and integration of architectural drawings

Recognition and integration of 2D architectural drawings provide a sound basis for automatically evaluating building designs, simulating safety, estimating construction cost or planning construction sequences. To accomplish these targets, difficulties come from (1) an architectural project is usually composed of a series of related drawings, (2) 3D information of structural objects may be expressed in 2D drawings, annotations, tables, or the composites of above expressions, and (3) a large number of disturbing graphical primitives in architectural drawings complicate the recognition processes. In this paper, we propose new methods to recognize typical structural objects and architectural symbols. Then the recognized results on the same floor and drawings of different floors will be integrated automatically for accurate 3D reconstruction.     

工程图中的模板识别和匹配方法

针对建筑结构图的规则和特征，提出了结构平面布置图和标准结构详图的识别及实例化方法。该方法分三个阶段：首先，通过表格识别和模板识别建立标准模板库；然后，利用规则引导识别结构平面图；最后，通过模板实例化实现构件到模板的对应。此方法可以大大提高工程图自动识别的正确性和系统的自适应能力。     

Orthonormal wavelet representations for recognizing complex annotations

This paper describes a novel method of pattern recognition targeted for recognizing complex annotations found in paper documents. Our investigation is motivated by the high reliability required for accomplishing autonomous interpretation of maps and engineering drawings. The recognition problem is made difficult in part because characters and text may be expressed in arbitrary fonts and orientations. Our approach includes a novel incremental strategy based on the multiscale representation of wavelet decompositions. Our approach is motivated by biological mechanisms of the human visual system. Choosing wavelets that are simultaneously localized in both space and frequency, and decomposing a signal into a multiscale hierarchical basis with orientation selectivity, can provide a powerful methodology for pattern analysis. We evaluated several wavelets with different spatial-frequency characteristics and measured their performance in the context of character recognition. Wavelet bases are more attractive than traditional hierarchical bases because they are orthonormal, linear, continuous, and continuously invertible. The multiscale representation of wavelet transforms provides a mathematically coherent basis for multigrid techniques. In contrast to previous adhoc approaches, our method promises a practical solution embedded in a unified mathematical theory. A feasibility study is described in which more than 10000 patterns were recognized with an error rate of 2.6% by a neural network trained using multiscale representations from a class of 52 distinct alphanumeric patterns and graphical symbols. We observed a 10-fold reduction in the amount of information needed to represent each character for recognition. These results suggest that high reliability is possible at a reduced cost of representation.     

机械工程图自动输入与计算机理解技术

工程图自动输入技术是近年来的研究热点之一，本文探讨了机械工程图计算机理解的层次及技术现状，给出了图形扫功与识别系统（ＧＩＲＳ）中直线，圆弧拟合及字符识别等的算法，并讨论了系统存在的不足及机械工程图理解的发展方向。     

Direction histogram: novel discriminative global feature for Thai offline handwritten OCR

The image feature used for classification is a crucial part of a character recognition system. To achieve a high accuracy of offline handwriting recognition, the feature should capture the essence of differences including the differences between different characters and the differences between different drawings of the same character. In this paper, we present a novel image feature called direction histogram (DH) and a feature extraction algorithm called bag of histogram (BoH). Unlike the traditional pre-defined feature, DH was designed based on the nature of language and the variation of writing styles. DH is, therefore, a global feature that represents pixel density in all directions around each center. BoH was introduced as it tolerates to thickness and curve variation and ignores the curve connectivity (if any). Fifty-two datasets, each containing 30 drawings of 80 Thai characters, are used for training our neural network, and the original, thick, and distorted handwriting datasets are used for testing. The recognition system with our proposed DH and BoH feature extraction algorithm yielded higher recognition accuracy compared to the convolutional neural network.     

Semi-supervised learning for character recognition in historical archive documents

Training recognizers for handwritten characters is still a very time consuming task involving tremendous amounts of manual annotations by experts. In this paper we present semi-supervised labeling strategies that are able to considerably reduce the human effort. We propose two different methods to label and later recognize characters in collections of historical archive documents. The first one is based on clustering of different feature representations and the second one incorporates a simultaneous retrieval on different representations. Hence, both approaches are based on multi-view learning and later apply a voting procedure for reliably propagating annotations to unlabeled data. We evaluate our methods on the MNIST database of handwritten digits and introduce a realistic application in form of a database of handwritten historical weather reports. The experiments show that our method is able to significantly reduce the human effort that is required to build a character recognizer for the data collection considered while still achieving recognition rates that are close to a supervised classification experiment.     

字符粘连及字线相交的分割与识别方法

描述了工程图纸矢量化中多向粘连字符及字线相交的分割算法与识别方法.提出不同情况下字串的定向计算方法,通过粘连字块的特征矢量计算和迭代计算实现字块的分割.运用波形投影方法解决了粘连字符及字线相交情况下的字间切割问题,使工程图多向字符识别精度显著提高,该算法对局部退化状态下的字符识别具有良好的抗干扰性.     

Online handwritten shape recognition using segmental hidden Markov models

We investigate a new approach for online handwritten shape recognition. Interesting features of this approach include learning without manual tuning, learning from very few training samples, incremental learning of characters, and adaptation to the user-specific needs. The proposed system can deal with two-dimensional graphical shapes such as Latin and Asian characters, command gestures, symbols, small drawings, and geometric shapes. It can be used as a building block for a series of recognition tasks with many applications