多模版匹配的特殊符号识别定位算法研究

针对试卷智能批阅场景模式,由于Tesseract-OCR缺少特殊符号包,直接定位符号存在较多漏检等问题,提出具有覆盖保留机制的多模板匹配方法。通过OCR定位空白试卷中的符号分别建立多类型元素的方块、圆圈、括号模板集;而对于试卷中的直线,通过筛选查找轮廓的方法建立多类型元素的直线模板集,综合多模板匹配技术提高试卷中符号的识别性能及定位准确率。经实际试卷测试结果表明:该算法符号定位准确率、精确度和召回率均高于94%;查找轮廓法定位直线准确率达96%,模板匹配直线定位准确率、精确度和召回率高于87%;将空白试卷符号坐标应用于学生作答试卷,能较完美地定位手写答案。     

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%.     

强噪声背景下图像标定符号定位算法研究

金属断口图像中标定符号信息是进一步计算图像对应实际物理空间距离的依据.标定符号通常为印刷体,所以准确定位是正确识别的前提和关键.对强噪声复杂背景下的金属断口图像标定符号的定位算法的研究,先对直线特征明显的标尺符号定位,其中对Radon变换进行分块改进,使快速性和准确性有了明显改善.字符定位利用符号的纹理特征进行数学形态学粗定位和图像边缘模板匹配精定位结合的方法,并根据标尺位置和长度等信息缩小搜索区域.实验结果表明,该算法的定位准确率达到94%.     

基于游程特征的选票符号识别方法

选票符号识别是基于图像理解的计票系统的关键技术,为提高选票符号识别的正确率,提出了一种基于游程特征的选票符号识别方法。首先给出了选票符号游程特征的定义,构建了选票符号的游程判定模型;然后利用三叉树结构描述了游程区域之间的相对位置关系;此外,通过游程区域的合并实现了噪声环境下主游程区域的提取,并对歧义符号的处理方法进行了研究;最后,实验结果表明,游程特征能够准确描述选票符号的几何特征,所给出的算法细分能力强,识别正确率高,比基于模板匹配的算法的正确率提高了6.07%。     

An Approach for Recognition and Interpretation of Mathematical Expressions in Printed Document

In this paper, we propose an approach for understanding Mathematical Expressions (MEs) in a printed document. The system is divided into three main components: (i) detection of MEs in a document; (ii) recognition of the symbols present in each ME; and (iii) arrangement of the recognised symbols. The MEs printed in separate lines are detected without any character recognition whereas the embedded expressions (mixed with normal text) are detected by recognising the mathematical symbols in text. Some structural features of the MEs are used for both cases. The mathematical symbols are grouped into two classes for convenience. At first, the frequently occurring symbols are recognised by a stroke-feature analysis technique. Recognition of less frequent symbols involves a hybrid of feature-based and template-based technique. The bounding-box coordinates and the size information of the symbols help to determine the spatial relationships among the symbols. A set of predefined rules is used to form the meaningful symbol groups so that a logical arrangement of the mathematical expression can be obtained. Experiments conducted using this approach on a large number of documents show high accuracy.     

手绘电路图形符号识别技术的研究

特征提取和分类器设计是手绘电路图形符号识别系统的关键环节。针对手绘图形不规则性的特点,提出了一种基于视觉的特征提取方法,并利用自适应学习速率的改进型BP神经网络进行分类识别。通过对10种手绘电路图形符号的分类实验,验证了文中设计的识别系统具有很好的分类效果和较强的实用性。     

MUSER: A prototype musical score recognition system using mathematical morphology

Music representation utilizes a fairly rich repertoire of symbols. These symbols appear on a score sheet with relatively little shape distortion, differing from the prototype symbol shapes mainly by a positional translation and scale change. The prototype system we describe in this article is aimed at recognizing printed music notation from digitized music score images. The recognition system is composed of two parts: a low-level vision module that uses morphological algorithms for symbol detection and a high-level module that utilizes prior knowledge of music notation to reason about spatial positions and spatial sequences of these symbols. The high-level module also employs verification procedures to check the veracity of the output of the morphological symbol recognizer. The system produces an ASCII representation of music scores that can be input to a music-editing system. Mathematical morphology provides us the theory and the tools to analyze shapes. This characteristic of mathematical morphology lends itself well to analyzing and subsequently recognizing music scores that are rich in well-defined musical symbols. Since morphological operations can be efficiently implemented in machine vision systems that have special hardware support, the recognition task can be performed in near real-time. The system achieves accuracy in excess of 95% on the sample scores processed so far with a peak accuracy of 99.7% for the quarter and eighth notes, demonstrating the efficacy of morphological techniques for shape extraction.     

A system for recognizing a large class of engineering drawings

We present a system for recognizing a large class of engineering drawings characterized by alternating instances of symbols and connection lines. The class includes domains such as flowcharts, logic and electrical circuits, and chemical plant diagrams. The output of the system, a netlist identifying the symbol types and interconnections, may be used for design simulation or as a compact portable representation of the drawing. The automatic recognition task is divided into two stages: 1) domain-independent rules are used to segment symbols from connection lines in the drawing image that has been thinned, vectorized, and preprocessed in routine ways; 2) a drawing understanding subsystem works in concert with a set of domain-specific matchers to classify symbols and correct errors automatically. A graphical user interface is provided to correct residual errors interactively and to log data for reporting errors objectively. The system has been tested on a database of 64 printed images drawn from text books and handbooks in different domains and scanned at 150 and 300 dpi resolution     

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%.     

MAGELLAN: Map Acquisition of GEographic Labels by Legend ANalysis

A system named MAGELLAN (denoting Map Acquisition of GEographic Labels by Legend ANalysis) is described that utilizes the symbolic knowledge found in the legend of the map to drive geographic symbol (or label) recognition. MAGELLAN first scans the geographic symbol layer(s) of the map. The legend of the map is located and segmented. The geographic symbols (i.e., labels) are identified, and their semantic meaning is attached. An initial training set library is constructed based on this information. The training set library is subsequently used to classify geographic symbols in input maps using statistical pattern recognition. User interaction is required at first to assist in constructing the training set library to account for variability in the symbols. The training set library is built dynamically by entering only instances that add information to it. MAGELLAN then proceeds to identify the geographic symbols in the input maps automatically. MAGELLAN can be fine-tuned by the user to suit specific needs. Recognition rates of over 93% were achieved in an experimental study on a large amount of data. Received January 5, 1998 / Revised March 18, 1998     

基于改进LSD和AP聚类的路径边缘识别策略

起重机金属结构攀爬机器人的路径边缘识别策略分为 3 个步骤。①图像预处理,利 用改进的过颜色算子进行灰度化;②使用基于支持向量机(SVM)最优分类线的方法确定梯度阈 值,并增设主方向角约束,改进线段分割检测(LSD)算法,得到直线段检测图像;③对直线段进 行特征提取,构建聚类数据集,基于数据集动态变化的特点,将基于先验信息的判别模型与近邻 传播(AP)聚类算法相结合,改进 AP 聚类算法,对直线段进行聚类,筛选出构成路径边缘的直线 段,并拟合得到最终的路径边缘线。实验结果表明,相较 AP 聚类和其他聚类算法,改进 AP 聚 类算法的筛选准确率最高;基于改进 LSD 和 AP 聚类的路径边缘识别策略的识别成功率为 96%, 且满足精度和实时性要求。     

基于改进 YOLOv5s 的离线手写数学符号识别

离线数学符号识别是离线数学表达式识别的前提。针对现有离线符号识别方法只是单纯的对符号进行识别,对离线表达式识别的其他环节未有任何帮助,反而会限制表达式识别,提出一种改进 YOLOv5s的离线符号识别方法。首先,根据符号图像小的特点,用生成对抗网络(GAN)进行数据增强;其次,从符号类别的角度分析,在 YOLOv5s 模型中引入空间注意力机制,利用全局最大值和全局平均值池化,扩大类别间的差异特征;最后,从符号自身角度分析,引入双向长短期记忆网络(BiLSTM)对符号特征矩阵进行处理,使符 号特征具有上下相关联的信息。实验结果表明：改进后的 YOLOv5s 取得较好离线符号识别效果,有 92.47%的识别率,与其他方法进行对比,证明了其有效性和稳健性。同时,能有效避免离线数学表达式识别中错误累积的问题,且能为表达式的结构分析提供有效依据。     

A graph grammar programming style for recognition of music notation

Graph grammars are a promising tool for solving picture processing problems. However, the application of graph grammars to diagram recognition has been limited to rather simple analysis of local symbol configurations. This paper introduces the Build-Weed-Incorporate programming style for graph grammars and shows its application in determining the meaning of complex diagrams, where the interaction among physically distant symbols is semantically important. Diagram recognition can be divided into two stages: symbol recognition and high-level recognition. Symbol recognition has been studied extensively in the literature. In this work we assume the existence of a symbol recognizer and use a graph grammar to assemble the diagram's information content from the symbols and their spatial relationships. The Build-Weed-Incorporate approach is demonstrated by a detailed discussion of a graph grammar for high-level recognition of music notation. See Appendix A for an illustration of the terms for musical symbols used in this paper.     

Rotation invariant hand-drawn symbol recognition based on a dynamic time warping model

One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes.     

Machine learning models for mathematical symbol recognition: A stem to stern literature analysis

Given the ubiquity of handwriting and mathematical content in human transactions, machine recognition of handwritten mathematical text and symbols has become a domain of great practical scope and significance. Recognition of mathematical expression (ME) has remained a challenging and emerging research domain, with mathematical symbol recognition (MSR) as a requisite step in the entire recognition process. Many variations in writing styles and existing dissimilarities among the wide range of symbols and recurring characters make the recognition tasks strenuous even for Optical Character Recognition. The past decade has witnessed the emergence of recognition techniques and the peaking interest of several researchers in this evolving domain. In light of the current research status associated with recognizing handwritten math symbols, a systematic review of the literature seems timely. This article seeks to provide a complete systematic analysis of recognition techniques, models, datasets, sub-stages, accuracy metrics, and accuracy details in an extracted form as described in the literature. A systematic literature review conducted in this study includes pragmatic studies until the year 2021, and the analysis reveals Support Vector Machine (SVM) to be the most dominating recognition technique and symbol recognition rate to be most frequently deployed accuracy measure and other interesting results in terms of segmentation, feature extraction and datasets involved are vividly represented. The statistics of mathematical symbols-related papers are shown, and open problems are identified for more advanced research. Our study focused on the key points of earlier research, present work, and the future direction of MSR.