Text detection in chart images

Common OCR (Optical Character Recognition) systems fail to detect and recognize small text strings of few characters, in particular when a text line is not horizontal. Such text regions are typical for chart images. In this paper we present an algorithm that is able to detect small text regions regardless of string orientation and font size or style. We propose to use this algorithm as a preprocessing step for text recognition with a common OCR engine. According to our experimental results, one can get up to 20 times better text recognition rate, and 15 times higher text recognition precision when the proposed algorithm is used to detect text location, size and orientation, before using an OCR system. Experiments have been performed on a benchmark set of 1000 chart images created with the XML/SWF Chart tool, which contain about 14000 text regions in total.     

工程扫描图象的直线整体识别算法

利用工程图纸的整体形状特性，提出了对扫描图象的直线整体识别算法，该算法对图象线条轮廓上的关键点进行了精确记录，并对它们进行了分析和处理。该算法不必单独处理线的交叉问题，但交叉点处的效果明显。它的基本思想是：首先检测原图象线宽，然后依据原图象线条的不同线宽，把原图象转化为一些由梯形块组成的条块图，并对不同的条块图分别进行整体识别     

基于神经网络的自然场景方向文本检测器

场景文本检测是场景文本识别中重要的一步,也是一个具有挑战性的问题。不同于一般的目标检测,场景文本检测的主要挑战在于自然场景图像中的文本具有任意方向,小的尺寸,以及多种宽高比。论文在TextBoxes[8]的基础上进行改进,提出了一个适用于任意方向文本的检测器,命名为OSTD(Oriented Scene Text Detector),可以有效且准确地检测自然场景中任意方向的文本。论文在公共数据集上对提出OSTD的进行评估。所有实验结果都表明,无论在准确性,还是实时性方面OSTD都是极具竞争力的方法。在1024×1024的ICDAR2015 Incidental Text数据集[16]上,OSTD的F-Measure=0.794,FPS=10.7。     

A*算法在Shortest-Path方面的优化研究

在游戏和地理信息系统开发等领域中,专门针对最短路径搜索方面的优化研究较多,尤其是最短路径中启发式搜索算法中的A*算法的效率优化研究.本文将针对在人工智能或算法研究中的使用的地图大多数是基于任意图而不是网格图的状况,通过任意图与网格图及方向的相结合,提出了三种优化A*算法的启发式函数搜索策略,较好地减小了算法搜索的范围和规模,有效地提高了A*算法的运行效率.最后的实验结果显示,与传统的A*算法相比较,优化启发搜索策略后的A*算法寻径更快速,更准确,计算效率更高.     

基于Hadoop的大规模网络安全实体识别方法

随着大数据时代的到来,如何从多源异构数据中准确地识别网络安全实体是构建网络安全知识图谱的基础问题。因此本文针对网络安全相关文本数据,研究支持海量网络数据的安全实体识别算法,为构建网络安全知识图谱奠定基础。针对海量的文本类网络数据中安全实体的高效精准抽取问题,本文基于Hadoop分布式计算框架提出改进的条件随机场（conditional random fields,CRF）算法,对数据集进行有效分割,实现安全实体的高效准确识别。在大规模真实网络数据集上的实验证明,本文提出的算法达到了较高的网络安全实体识别准确率,同时提高了识别的效率。     

旋转角可变的人体检测算法

常用的人体检测算法多应用于无旋转角的情况,而在旋转角可变的情况下检测性能有限,为此提出了一种适用于有旋转角的人体检测算法。首先,通过径向梯度转换(RGT)获得具有旋转不变性的梯度;其次,使用类似于梯度方向直方图(HOG)特征中相互重叠块的组合方式,获取多个带有旋转角信息的特征描述子,按旋转角大小将它们一维线性连接成具有旋转不变性的特征描述子组;最后,利用基于支持向量机(SVM)的二级级联分类器实现了带旋转角的人体检测。基于INRIA行人数据库的144个不同旋转角的人体测试集检测率都不低于86%,144个不同旋转角的非人体样本误检率均低于10%。实验证明了该算法可用于在任意旋转角图像上进行人体检测。     

PDF文档HTML化中文本重排问题研究

目前各种PDF转化工具中,将PDF元素抽取后还原顺序的方法是根据每个文字元素的坐标——由左到右,由上到下的顺序重排元素。这种重排方式无法正确还原多栏或者多区域的PDF文档。文章提出了一种页面分块算法。所提算法将页面划分为不同的区域,在分区基础上重排,有效的提高了多栏或者多区域的PDF文档文本顺序还原的正确性。     

Address block location on envelopes using Gabor filters

The large volume of mail and the increased cost of handling it has made postal automation an important domain for pattern recognition and computer vision research. A substantial amount of work is being done to design an automatic mail sorting system which can read and interpret the destination address on a mail piece and direct it to the appropriate bin. Robust optical character recognition (OCR) systems are now available which can read printed characters with great accuracy (> 99%). But, in order to read the destination address, the region in the image containing the address must first be located. Even though several approaches to address block location have been proposed in the literature, it remains a difficult problem. A simple method is presented for automatically identifying regions in envelope images which are candidates for being the destination address. The envelope image is considered to contain different textured regions, one of which corresponds to the text-content in the image. Thus, a texture-based segmentation method is used to identify the regions of text in the image. The method for texture discrimination is based on Gabor filters which have been successfully used earlier for a variety of texture classification and segmentation tasks. It is shown that only a small number of even-symmetric Gabor filters are needed in this application. The success of the texture-based segmentation algorithm for identifying address blocks is demonstrated on a number of test images. These results also demonstrate the invariance of the method to the orientation of text in the envelope image and the variations in the size and font of the text.     

基于HOG-SVM的跳频信号检测识别算法

针对非合作通信场景下的跳频信号自动化检测识别问题,本文提出了一种基于方向梯度直方图与支持向量机的跳频信号检测识别算法。该算法将无线通信信号转化为包含时间、频率和幅度的时频瀑布图,采用方向梯度直方图特征提取算法将不同跳频序列在瀑布图上产生的独特结构特征提取出来。然后利用支持向量机将特征序列映射到高维空间,通过寻找最大间隔分离超平面,实现跳频信号的检测与多种跳频序列的识别,并依此建立跳频信号检测识别原型系统。最后在室内多径信道环境下进行了测试验证,该算法能够完全自动化的精确检测到开放电磁环境下的跳频信号并且能够实现对多种跳频序列的识别。在信干噪比不超过20dB时,针对不同跳频序列的平均识别正确率能够达到98.01%。     

面向中文文本倾向性分类的对抗样本生成方法

研究表明，在深度神经网络（DNN）的输入中添加小的扰动信息，能够使得DNN出现误判，这种攻击被称为对抗样本攻击.而对抗样本攻击也存在于基于DNN的中文文本的情感倾向性检测中，因此提出了一种面向中文文本的对抗样本生成方法WordHanding.该方法设计了新的词语重要性计算算法，并用同音词替换以生成对抗样本，用于在黑盒情况下实施对抗样本攻击.采用真实的数据集（京东购物评论和携程酒店评论），在长短记忆网络（LSTM）和卷积神经网络（CNN）这两种DNN模型上验证该方法的有效性.实验结果表明，生成的对抗样本能够很好地误导中文文本的倾向性检测系统.     

一种轻量化中文指路标志的文本识别算法

针对中文交通指路标志中多方向、多角度的文本提取与识别困难的问题,提出了一种融合了卷积神经网络与传统机器学习方法的轻量化中文交通指路标志文本提取与识别算法。首先,对YOLOv5l目标检测网络进行轻量改进,提出了YOLOv5t网络用以提取指路标志牌中的文本区域;然后,结合投影直方图法与多项式拟合法的M-split算法,对提取到的文本区域进行字符分割;最后,使用MobileNetV3轻量化网络对文本进行识别。提出的算法在自制数据集TS-Detect上进行近景文本识别,精度达到了901%,检测速度达到了40 fps,且权重文件大小仅有24.45 MB。实验结果表明,提出的算法具有轻量化、高精度的特性,能够完成复杂拍摄条件下的实时中文指路标志文本提取与识别任务。     

一种直接高效的自然场景汉字逼近定位方法

为了提高经典目标检测算法对自然场景文本定位的准确性,以及克服传统字符检测模型由于笔画间存在非连通性引起的汉字错误分割问题,提出了一种直接高效的自然场景汉字逼近定位方法。采用经典的EAST算法对场景图像中的文字进行检测。对初检的文字框进行调整使其更紧凑和更完整地包含文字,主要由提取各连通笔画成分、汉字分割和文字形状逼近三部分组成。矫正文字区域和识别文字内容。实验结果表明,提出的算法在保持平均帧率为3.1 帧/s的同时,对ICDAR2015、ICDAR2017-MLT和MSRA-TD500三个多方向数据集上文本定位任务中的F-score分别达到83.5%、72.8%和81.1%;消融实验验证了算法中各模块的有效性。在ICDAR2015数据集上的检测和识别综合评估任务中的性能也验证了该方法相比一些最新方法取得了更好的性能。     

Optimizing layer‐based scheduling algorithms for parallel tasks with dependencies

Programming with parallel tasks leads to task graphs with dependencies representing a parallel program. Scheduling algorithms are employed to find an efficient execution order of the parallel tasks. A large variety of scheduling algorithms exist, including layer‐based scheduling algorithms for homogeneous target platforms that build consecutive layers of independent parallel tasks and schedule each layer separately. Although these scheduling algorithms provide good results in terms of scheduling algorithm runtime and schedule execution time, the resulting schedules leave room for optimization. This article proposes an optimization for arbitrary layer‐based scheduling algorithms, which is called Move‐blocks algorithm. Given a layer‐based schedule of the parallel tasks, this algorithm moves blocks of parallel tasks into preceding layers in order to reduce the overall execution time of a task‐based application. Suitable blocks of parallel tasks are identified by the algorithm Find‐blocks, which is employed together with the Move‐blocks algorithm. The algorithm Move‐blocks is applied to four well‐known scheduling algorithms. A detailed evaluation for a wide range of test cases is given. Copyright © 2010 John Wiley & Sons, Ltd.     

CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

In today’s real world, an important research part in image processing is scene text detection and recognition. Scene text can be in different languages, fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios and layouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes. In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules incorporated Convolutional Neural Network (CR-CNN) has been proposed. Moreover, we have recommended a new text detection method for detecting the exact text from the input natural scene images. For enhancing the presentation of the edge detection process, image pre-processing activities such as edge detection and color modeling have beenapplied in this work. In addition, we have generated new fuzzy rules for making effective decisions on the processes of text detection and recognition. The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR 2005 and the SVT and have achieved better detection accuracy intext detection and recognition. By using these three datasets, five different experiments have been conducted for evaluating the proposed model. And also, we have compared the proposed system with the other classifiers such as the SVM, the MLP and the CNN. In these comparisons, the proposed model has achieved better classification accuracywhen compared with the other existing works.     

一种视频文本自动定位、跟踪和识别的方法

视频数据中的文本能提供重要的语义信息。本文提出了一种视频文本自动定位、跟踪和识别的方法，首先用基于小波和LH检测视频帧文本所在的位置，然后用运动估计的方法，跟踪后继帧文本的位置，再用多帧平均的方法增强文本区域，最后经过二值化处理和连通分量分析，将文本字符送入OCR软件进行识别。实验结果表明，该方法简单易行，能快速地定位和跟踪文本区域，定位精度和识别效果良好。     

一种鲁棒的指纹奇异点检测方法

提出一种两阶段的奇异点检测方法。将指纹图像分块,求出各块的方向构成块方向图,并在块方向图的基础上利用邻域方向的分布分析结合改进的Poincare Index方法来确定奇异点所在的候选区域,对候选区域中的像素再通过计算局部方向变化率来确定奇异点的精确位置。将此方法用于对FVC2004 DB1_A指纹数据库的图像,实验结果表明这种方法对指纹图像中的噪声有很好的鲁棒性,并且计算简单快速,易于实现。     

像素聚合和特征增强的任意形状场景文本检测

目的 获取场景图像中的文本信息对理解场景内容具有重要意义,而文本检测是文本识别、理解的基础。为了解决场景文本识别中文字定位不准确的问题,本文提出了一种高效的任意形状文本检测器：非局部像素聚合网络。方法 该方法使用特征金字塔增强模块和特征融合模块进行轻量级特征提取,保证了速度优势;同时引入非局部操作以增强骨干网络的特征提取能力,使其检测准确性得以提高。非局部操作是一种注意力机制,能捕捉到文本像素之间的内在关系。此外,本文设计了一种特征向量融合模块,用于融合不同尺度的特征图,使尺度多变的场景文本实例的特征表达得到增强。结果 本文方法在3个场景文本数据集上与其他方法进行了比较,在速度和准确度上均表现突出。在ICDAR（International Conference on Document Analysis and Recognition） 2015数据集上,本文方法比最优方法的F值提高了0.9%,检测速度达到了23.1 帧/s;在CTW（Curve Text in the Wild） 1500数据集上,本文方法比最优方法的F值提高了1.2%,检测速度达到了71.8 帧/s;在Total-Text数据集上,本文方法比最优方法的F值提高了1.3%,检测速度达到了34.3 帧/s,远远超出其他方法。结论 本文方法兼顾了准确性和实时性,在准确度和速度上均达到较高水平。     

An automated approach for the recognition of intended messages in grouped bar charts

Information graphics (bar charts, line graphs, grouped bar charts, etc) often appear in popular media such as newspapers and magazines. In most cases, the information graphic is intended to convey a high‐level message. This message plays a role in facilitating the discourse purpose of the document but is seldom repeated in the document's text, headlines, or captions. We present a methodology and an implemented system for recognizing the intended message of a grouped bar chart. The recognition system relies on the following components: (1) a linguistic classifier that processes text in the graphic and predicts the most linguistically salient entity from those that are mentioned in text, (2) a cognitive model that estimates the relative perceptual effort required for an individual to recognize some high‐level message in a graph, and (3) a Bayesian network that captures the probabilistic relationship between the high‐level intended message of a graphic and its communicative signals. This research contributes to three applications: accessibility of information graphics for sight‐impaired individuals, retrieval of information graphics from a digital library, and summarization of multimodal documents.     

基于蚁群算法的分布多处理机系统任务调度研究

本文主要基于现代蚁群算法讨论分布式系统调度。蚁群算法是一种构造型启发算法,在离散优化问题中得到广泛应用。分布式系统调度属于NP-hard,为了提高算法性能,把问题任务图的优先级作为启发信息。最后,采用随机产生的任务图将调度结果和模拟退火算法、遗传算法等进行了比较。     

Analysis and recognition of highly degraded printed characters

This paper proposes an integrated system for the processing and analysis of highly degraded printed documents for the purpose of recognizing text characters. As a case study, ancient printed texts are considered. The system is comprised of various blocks operating sequentially. Starting with a single page of the document, the background noise is reduced by wavelet-based decomposition and filtering, the text lines are detected, extracted, and segmented by a simple and fast adaptive thresholding into blobs corresponding to characters, and the various blobs are analyzed by a feedforward multilayer neural network trained with a back-propagation algorithm. For each character, the probability associated with the recognition is then used as a discriminating parameter that determines the automatic activation of a feedback process, leading the system back to a block for refining segmentation. This block acts only on the small portions of the text where the recognition cannot be relied on and makes use of blind deconvolution and MRF-based segmentation techniques whose high complexity is greatly reduced when applied to a few subimages of small size. The experimental results highlight that the proposed system performs a very precise segmentation of the characters and then a highly effective recognition of even strongly degraded texts.