应用OCR技术的金融票据处理系统

针对日常应用的金融票据,我们使用计算机进行自动处理。在金融票据自动处理系统的多个环节,我们应用了OCR技术。在票据类型识别这一重要环节,我们对印刷体的表头信息进行OCR识别,进一步提高了对票据类型的识别率。我们还对表格中以中文大写数字,手写阿拉伯数字,勾选填写的信息项运用OCR技术进行处理,提高了票据自动处理系统的能力。     

提高OCR扫描识别率有诀窍

扫描仪在日常使用中的一个重要功能是OCR，是电子设备检测打印在纸上的字符，并通过其亮暗模式来确定形状的方法，经扫描仪确定了字符的形状后，会使用字符识别方法将形状转换成计算机文本。但是在OCR识别的过程中，往往会出现许多识别错误的情况，下面笔者将向你介绍在使用中的几点经验。1、选择高识别率的专业OCR软件购买扫描仪时，附带的扫描软件中普遍都提供OCR识别软件，但是其识别率很难令人满意，所以一般应当使用专业的OCR识别软件。2、在识别前一定要作版面分析和倾斜校正以尚书OCR为例，其版面分析把文稿分为横排正文、竖…     

票据识别技术研究进展

本文对银行的票据自动识别技术及其进展进行了论述。主要是票据自动识别系统的构建,包括扫描输入模块和识别模块。介绍OCR在票据自动识别系统中的应用,介绍一些最新票据识别算法,主要是通用票据识别系统中的字符切分方法、基于特征线检取的票据识别算法和滴水算法。     

一种基于形态变换的复杂背景字符笔画提取方法

OCR软件对图像背景的字符的处理能力有限,为了提高OCR的识别率必须对字符进行预处理。该文提出采用SUSAN拐角检测算法生成图像字符区域的拐角响应图,然后利用拐角过滤算法去除错误的拐角响应生成字符候选区域,最后应用了形态数学变换将字符笔画精确地分离出。经实验检验本算法较好地完成字符笔画提取,是一种提高OCR软件识别率的有效方法。     

用于脱机手写数字识别的隐马尔可夫模型

将隐马尔可夫模型(HMM)用于脱机手写数字识别中，系统如何建模是一个值得研究的问题．在考虑手写数字自身特点及特征抽取的基础上，对HMM模型的训练方法及模型参数的选取进行了研究，以提高系统识别率．在银行票据OCR的应用中，与基于神经网络的方法结合使用，使得整张票据的拒识率降低了3％，明显提高了银行票据OCR系统的性能．     

层次型金融票据图像分类方法

金融票据图像识别处理是当今的一个热点研究方向,而票据分类是其中重要的基础部分。针对种类繁多、数量巨大、版面复杂和噪声干扰严重的金融票据彩色图像,本文提出了一种基于二叉树决策的层次型票据类型匹配方法。该方法利用三个类型判断器:基于票据版面结构的松弛匹配、基于OCR 的票据标题识别和基于票据颜色的色彩分析,层次化的进行票据类型判断。实验表明,层次型金融票据图像分类方法具有良好的效果;基于该方法的银行票据识别处理系统已经广泛应用于各大银行的相关业务系统中。     

基于知识的银行票据二值化方法

本文结合银行票据OCR系统的开发,提出一种基于知识进行银行票据二值化的新思路,并针对各类识别域具体构造了一整套二值化方法。通过在银行票据OCR系统中的应用,验证了本文二值化方法的效果。     

紫光OCR 7.0识“手写”

在国内 OCR产品中,清华紫光 OCR 一直以98％以上的识别率和优良的售后服务而被业界所推崇,连续几年一直保持销售第一的业绩。继OCR6.0之后,紫光集团近日向业界推出最新的识别软件——紫光 OCR 7.0版,它不仅保留了原有功能,而且可以对手写体进行识别。OCR 是与扫描仪配套     

Mini OCR

目前所有扫描仪都附带了专业的OCR识别软件，可以把扫描仪扫进电脑中的图像中的文字识别出来，但这些商业软件对于我们从屏幕上抓取的包含文字的截图，识别率非常差。有什么软件可以解决这个问题呢？Mini OCR就可以。     

基于灰度差分和二维最大熵阈值的新闻字幕检测*

针对新闻视频帧中文本区域的定位提取问题,提出了一种有效的字幕定位提取方法。通过灰度差分和变异灰度直方图对新闻视频帧字幕区域定位,再经改进的二维最大熵阈值方法对分割出的文字区域进行二值化,得到可识别的文字图片。最后对文本定位和OCR识别情况进行了算法对比。实验表明：与传统的投影法和最大熵方法相比,该方法可有效地提高文本定位的查全率和OCR的识别率。     

OCR error correction using correction patterns and self-organizing migrating algorithm

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the errors. In this paper, we present an automatic model consisting of both error detection and error correction phases for OCR post-processing. We propose a novel approach of OCR post-processing error correction using correction pattern edits and evolutionary algorithm which has been mainly used for solving optimization problems. Our model adopts a variant of the self-organizing migrating algorithm along with a fitness function based on modifications of important linguistic features. We illustrate how to construct the table of correction pattern edits involving all types of edit operations and being directly learned from the training dataset. Through efficient settings of the algorithm parameters, our model can be performed with high-quality candidate generation and error correction. The experimental results show that our proposed approach outperforms various baseline approaches as evaluated on the benchmark dataset of ICDAR 2017 Post-OCR text correction competition.

     

An experimental evaluation of OCR text representations for learning document classifiers

In the literature, many feature types are proposed for document classification. However, an extensive and systematic evaluation of the various approaches has not yet been done. In particular, evaluations on OCR documents are very rare. In this paper we investigate seven text representations based on n-grams and single words. We compare their effectiveness in classifying OCR texts and the corresponding correct ASCII texts in two domains: business letters and abstracts of technical reports. Our results indicate that the use of n-grams is an attractive technique which can even compare to techniques relying on a morphological analysis. This holds for OCR texts as well as for correct ASCII texts. Received February 17, 1998 / Revised April 8, 1998     

Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)

Optical Character Recognition (OCR) is the process of recognizing printed or handwritten text on paper documents. This paper proposes an OCR system for Arabic characters. In addition to the preprocessing phase, the proposed recognition system consists mainly of three phases. In the first phase, we employ word segmentation to extract characters. In the second phase, Histograms of Oriented Gradient (HOG) are used for feature extraction. The final phase employs Support Vector Machine (SVM) for classifying characters. We have applied the proposed method for the recognition of Jordanian city, town, and village names as a case study, in addition to many other words that offers the characters shapes that are not covered with Jordan cites. The set has carefully been selected to include every Arabic character in its all four forms. To this end, we have built our own dataset consisting of more than 43.000 handwritten Arabic words (30000 used in the training stage and 13000 used in the testing stage). Experimental results showed a great success of our recognition method compared to the state of the art techniques, where we could achieve very high recognition rates exceeding 99%.     

Learning on the fly: a font-free approach toward multilingual OCR

Despite ubiquitous claims that optical character recognition (OCR) is a “solved problem,” many categories of documents continue to break modern OCR software such as documents with moderate degradation or unusual fonts. Many approaches rely on pre-computed or stored character models, but these are vulnerable to cases when the font of a particular document was not part of the training set or when there is so much noise in a document that the font model becomes weak. To address these difficult cases, we present a form of iterative contextual modeling that learns character models directly from the document it is trying to recognize. We use these learned models both to segment the characters and to recognize them in an incremental, iterative process. We present results comparable with those of a commercial OCR system on a subset of characters from a difficult test document in both English and Greek.     

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Text embedded in multimedia documents represents an important semantic information that helps to automatically access the content. This paper proposes two neural-based optical character recognition (OCR) systems that handle the text recognition problem in different ways. The first approach segments a text image into individual characters before recognizing them, while the second one avoids the segmentation step by integrating a multi-scale scanning scheme that allows to jointly localize and recognize characters at each position and scale. Some linguistic knowledge is also incorporated into the proposed schemes to remove errors due to recognition confusions. Both OCR systems are applied to caption texts embedded in videos and in natural scene images and provide outstanding results showing that the proposed approaches outperform the state-of-the-art methods.     

Font adaptive word indexing of modern printed documents

We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of self organizing maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.     

A prototype document image analysis system for technical journals

Gobbledoc, a system providing remote access to stored documents, which is based on syntactic document analysis and optical character recognition (OCR), is discussed. In Gobbledoc, image processing, document analysis, and OCR operations take place in batch mode when the documents are acquired. The document image acquisition process and the knowledge base that must be entered into the system to process a family of page images are described. The process by which the X-Y tree data structure converts a 2-D page-segmentation problem into a series of 1-D string-parsing problems that can be tackled using conventional compiler tools is also described. Syntactic analysis is used in Gobbledoc to divide each page into labeled rectangular blocks. Blocks labeled text are converted by OCR to obtain a secondary (ASCII) document representation. Since such symbolic files are better suited for computerized search than for human access to the document content and because too many visual layout clues are lost in the OCR process (including some special characters), Gobbledoc preserves the original block images for human browsing. Storage, networking, and display issues specific to document images are also discussed     

Text detection and recognition in images and video frames

This paper presents a new method for detecting and recognizing text in complex images and video frames. Text detection is performed in a two-step approach that combines the speed of a text localization step, enabling text size normalization, with the strength of a machine learning text verification step applied on background independent features. Text recognition, applied on the detected text lines, is addressed by a text segmentation step followed by an traditional OCR algorithm within a multi-hypotheses framework relying on multiple segments, language modeling and OCR statistics. Experiments conducted on large databases of real broadcast documents demonstrate the validity of our approach.     

一种基于页眉线的扭曲文档图像快速校正方法

在对文档图像进行光学字符识别时,由于书籍扭曲的存在,识别率会降低。对于 含有页眉页脚线的扭曲文档图像,提出一种快速校正方法。首先分别检测并定位图像中的页眉 线,保存页眉线的坐标信息。根据等比算法计算页眉线上各点在校正时所需向上或向下移动的 距离,然后以此距离为参数扫描图像,计算页眉页脚线之间的各个目标像素校正所需移动的距 离,同时进行像素点的移动重构图像,最终得到校正的图像。实验结果表明,该方法校正效果明显, 对于包含页眉页脚线的扭曲文档图像有较好的校正效果,校正后OCR 识别率大幅度提高。