OCR RECOGNITION SYSTEMS

High-volume data entry jobs are common in such vertical market applications as insurance claims processing and tax reporting; however, the task of effectively reading the data that is hand-printed on various paper forms remains a challenge. The reading capabilities of two types of optical character recognition (OCR) systems are improving the accuracy and reducing the cost of large data entry jobs.     

Automated evaluation of OCR zoning

Many current optical character recognition (OCR) systems attempt to decompose printed pages into a set of zones, each containing a single column of text, before converting the characters into coded form. The authors present a methodology for automatically assessing the accuracy of such decompositions, and demonstrate its use in evaluating six OCR systems     

Adaptive classifiers for multisource OCR

When patterns occur in large groups generated by a single source (style consistent test data), the statistics of the test data differ from those of the training data, which consist of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop adaptive classifiers that specialize in the statistics of style-consistent test data. On NIST handwritten digit data, the adaptive classifiers reduce the error rate by more than 50% operating on one writer ( samples/class) at a time.Received: 14 November 2002, Accepted: 6 March 2003, Published online: 12 September 2003Correspondence to: George Nagy     

N-tuple features for OCR revisited

N-tuple features for optical character recognition have received only scattered attention since the 1960s. Our main purpose is to show that advances in computer technology and computer science compel renewed interest. N-tuple features are useful for printed character classification because they indicate the presence or absence of a given rigid configuration of n black and white pixels in a pattern. Desirable n-tuples fit each pattern of a specified (positive) training set of characters in at least p different shift positions, and fail to fit each pattern of a specified (negative) training set by at least n-q pixels in each shift position. We prove that the problem of finding a distinguishing n-tuple is NP-complete, by examining a natural subproblem with binary strings called the missing configuration problem. The NP-completeness result notwithstanding, distinguishing n-tuples are found automatically in a few seconds on contemporary workstations. We exhibit a practical search algorithm for generating, from a small training set, a collection of n-tuples with low class-conditional correlation and with specified design parameters n, p, and q. The generator, which is available on the Internet, is empirically shown to be effective through a comparison with a benchmark generator. We show experimentally that the design parameters provide a useful tradeoff between distinguishing power and generation time, and also between the conditional probabilities for the positive and negative classes. We explore the feature probabilities obtainable for various dichotomies, and show that the design parameters control the feature probabilities     

Retrieving poorly degraded OCR documents

A significant portion of currently available documents exist in the form of images, for instance, as scanned documents. Electronic documents produced by scanning and OCR software contain recognition errors. This paper uses an automatic approach to examine the selection and the effectiveness of searching techniques for possible erroneous terms for query expansion. The proposed method consists of two basic steps. In the first step, confused characters in erroneous words are located and editing operations are applied to create a collection of erroneous error-grams in the basic unit of the model. The second step uses query terms and error-grams to generate additional query terms, identify appropriate matching terms, and determine the degree of relevance of retrieved document images to the user's query, based on a vector space IR model. The proposed approach has been trained on 979 document images to construct about 2,822 error-grams and tested on 100 scanned Web pages, 200 advertisements and manuals, and 700 degraded images. The performance of our method is evaluated experimentally by determining retrieval effectiveness with respect to recall and precision. The results obtained show its effectiveness and indicate an improvement over standard methods such as vectorial systems without expanded query and 3-gram overlapping.     

OCR in a hierarchical feature space

This paper describes hierarchical OCR, a character recognition methodology that achieves high speed and accuracy by using a multiresolution and hierarchical feature space. Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. Typically, recognizers have to balance the use of features at many resolutions (which yields a high accuracy), with the burden on computational resources in terms of storage space and processing time. We present in this paper, a method that adaptively determines the degree of resolution necessary in order to classify an input pattern. This leads to optimal use of computational resources. The hierarchical OCR dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against, and the processing time available. Furthermore, the finer resolution is accorded to only certain “zones” of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. When tested on standard NIST data sets, the hierarchical OCR proves to be 300 times faster than a traditional K-nearest-neighbor classification method, and 10 times taster than a neural network method. The comparison uses the same feature set for all methods. Recognition rate of about 96 percent is achieved by the hierarchical OCR. This is at par with the other two traditional methods     

基于 OCR 技术的图像验证码识别

验证码是一种标准的网络安全技术,它主要用来防止网民或者黑客等对网站的恶意注册和访问,以及发送垃圾文件、暴力破解高价值密码、滥发广告等恶意事件。通过对图片的扫描,可以提取图片中的数字、字符信息等。本文提出了一种基于OCR技术的图形验证码识别技术。通过对验证码图片进行灰度化、二值化、去噪点、圈点填充、直线填充、图像分割、统一大小、图像匹配和存入字库等一系列的操作,进行对验证码的识别实验。本文通过实验对验证码的特点有了充分的了解,这样方便设计出更加安全的验证码,防止被不法分子破解。     

利用OCR识别技术实现视频中文字的提取

为了在视频图像中进行字幕信息的实时提取,提出了一套简捷而有效的方法。首先进行文字事件检测,然后进行边缘检测、阈值计算和边缘尺寸限制,最后依据文字像素密度范围进一步滤去非文字区域的视频字幕,提出的叠加水平和垂直方向边缘的方法,加强了检测到的文字的边缘;对边缘进行尺寸限制过滤掉了不符合文字尺寸的边缘。应用投影法最终确定视频字幕所在区域。最后,利用OCR识别技术对提取出来的文字区域进行识别,完成视频中文字的提取。以上方法的结合保证了提出算法的正确率和鲁棒性。     

Using topic models for OCR correction

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.     

Summarization of Imaged Documents without OCR

A system is presented for creating a summary indicating the contents of an imaged document. The summary is composed from selected regions extracted from the imaged document. The regions may include sentences, key phrases, headings, and figures. The extracts are identified without the use of optical character recognition. The imaged document is first processed to identify the word-bounding boxes, the reading order of words, and the location of sentence and paragraph boundaries in the text. The word-bounding boxes are grouped into equivalence classes to mimic the terms in a text document. Equivalence classes representing content words are identified, and key phrases are identified from the set of content words. Summary sentences are selected using a statistically based classifier applied to a set of discrete sentence features. Evaluation of sentence selection against a set of abstracts created by a professional abstracting company is given.     

Imaged document text retrieval without OCR

We propose a method for text retrieval from document images without the use of OCR. Documents are segmented into character objects. Image features, namely the vertical traverse density (VTD) and horizontal traverse density (HTD), are extracted. An n-gram-based document vector is constructed for each document based on these features. Text similarity between documents is then measured by calculating the dot product of the document vectors. Testing with seven corpora of imaged textual documents in English and Chinese as well as images from the UW1 (University of Washington 1) database confirms the validity of the proposed method     

Slant estimation algorithm for OCR systems

A slant removal algorithm is presented based on the use of the vertical projection profile of word images and the Wigner–Ville distribution. The slant correction does not affect the connectivity of the word and the resulting words are natural. The evaluation of our algorithm was equally made by subjective and objective means. The algorithm has been tested in English and Modern Greek samples of more than 500 writers, taken from the databases IAM-DB and GRUHD. The extracted results are natural, and almost always improved with respect to the original image, even in the case of variant-slanted writing. The performance of an existed character recognition system showed an increase of up to 9% for the same data, while the training time cost was significantly reduced. Due to its simplicity, this algorithm can be easily incorporated into any optical character recognition system.     

Hand-scan OCR with a one-dimensional image sensor

We have developed a hand scanning type OCR, an optical character reader, which is applicable to POS terminals, etc. It uses a one-dimensional image sensor for small size and low cost. We developed a new recognition method which can overcome the irregularity of the hand scanning. Our method has two kinds of counters to extract the horizontal line features. One is a black occurrence counter and the other is a front shape counter. Normalizing these data reduces the influence of pattern variation. The method has been tested widely all over the world as a commercial OCR-A font reader.     

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．     

面向嵌入式系统的OCR算法

提出了一个快速有效的统计类型算法,避免了采用传统的OCR结构分析方法所需要进行的大量腐蚀运算等图形处理工作。因此该算法在时间以及计算复杂度等方面都远远优于原来的结构分析法,并用C＋＋语言加以实现,在实践中取得了很好的效果。     

An Overview of Neural OCR Networks

Because neural networks specialize in handling ambiguous data, they are especially suited for such applications as speech recognition and optical character recognition (OCR). OCR applications are usually ambiguous because their data is generated by an inconsistent factor—the individual. This article provides an overview of neural networks and describes how this technology can be integrated with OCR technology to create neural OCR networks that can significantly improve the process of optical character recognition.     

An Overview of Neural OCR Networks

Abstract

Because neural networks specialize in handling ambiguous data, they are especially suited for such applications as speech recognition and optical character recognition (OCR). OCR applications are usually ambiguous because their data is generated by an inconsistent factor—the individual. This article provides an overview of neural networks and describes how this technology can be integrated with OCR technology to create neural OCR networks that can significantly improve the process of optical character recognition.     

通用OCR开发工具的设计

提出了一个通用OCR开发工具的设想,用于各种文字的OCR软件的开发,它能够在使用者的干预下自动完成识别器的设计,大大减少文字识别软件开发的工作量。系统以决策树作为基本的判别器,并用多个决策树组成多方案识别系统。提出设计树和分类器设计器的概念,分别用于决策树设计过程的控制和决策树节点中的分类器的设计。最后实现一个实验系统,验证了该文的设想和设计方案的可行性。     

Feature selection for low error rate OCR

A scheme suitable for selection of features for OCR systems has been developed. This scheme has been successfully applied to several type fonts resulting in systems which recognize upper and lower case alphanumerics with less than one error per 10 000 processed characters. Data based on a large number of test characters is collected and formatted to provide a basis for the actual selection of features. The error rate for the resulting recognition systems is then verified. Considerable portions of this process have been automated, while retaining adequate opportunity for the OCR systems designer to control and influence the selection process.