集成汉英OCR系统识别中文名片

汉英双语混排识别是构造中文自动文档图像处理系统时常会遇到的一个问题。只有采用一种有效的方法集成现有汉英识别引擎,才可能高质量地识别混排文档。该文应用适当干预和多层次语言判断的汉英OCR系统集成原则,集成OCR系统识别中文名片。实验数据表明,利用该原则构造的系统确实能有效集成汉英识别引擎,在纯中文识别率为89.86%,纯英文识别率为91.20%的情况下,使名片最终总体识别率达到了93.45%,较好地解决了汉英混排名片的识别问题。     

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.     

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.

     

Recognition-based character segmentation for multi-level writing style

International Journal on Document Analysis and Recognition (IJDAR) - Character segmentation is an important task in optical character recognition (OCR). The quality of any OCR system is highly...     

Robust named entity detection from optical character recognition output

In this paper, we focus on information extraction from optical character recognition (OCR) output. Since the content from OCR inherently has many errors, we present robust algorithms for information extraction from OCR lattices instead of merely looking them up in the top-choice (1-best) OCR output. Specifically, we address the challenge of named entity detection in noisy OCR output and show that searching for named entities in the recognition lattice significantly improves detection accuracy over 1-best search. While lattice-based named entity (NE) detection improves NE recall from OCR output, there are two problems with this approach: (1) the number of false alarms can be prohibitive for certain applications and (2) lattice-based search is computationally more expensive than 1-best NE lookup. To mitigate the above challenges, we present techniques for reducing false alarms using confidence measures and for reducing the amount of computation involved in performing the NE search. Furthermore, to demonstrate that our techniques are applicable across multiple domains and languages, we experiment with optical character recognition systems for videotext in English and scanned handwritten text in Arabic.     

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。     

小波的文本图像区分及其在文献信息数字化中的应用

目前,OCR技术对文本图像区域自动区分的效果还不够精确,进而影响了OCR技术在文献信息数字化过程中的工作效率.针对这一局限,提出了一种基于小波的文本图像区分方法.方法首先对扫描区域进行小波分解,然后使用分解系数构建分解能量,最后依据分解能量大小对文本图像进行自动区分.结果表明,该方法对文本图像的区分效果较好,减少了在使用OCR技术进行文献信息数字化时的人为干预,有利于提高文献信息数字化过程的自动化水平.最后通过实验仿真验证了该方法的有效性.     

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.     

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     

An omnifont open-vocabulary OCR system for English and Arabic

We present an omnifont, unlimited-vocabulary OCR system for English and Arabic. The system is based on hidden Markov models (HMM), an approach that has proven to be very successful in the area of automatic speech recognition. We focus on two aspects of the OCR system. First, we address the issue of how to perform OCR on omnifont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is used to train a single model, becomes an important issue in the face of the conditional independence assumption inherent in the use of HMMs. We demonstrate mathematically and empirically how to allocate training data among the different styles to alleviate this problem. Second, we show how to use a word-based HMM system to perform character recognition with unlimited vocabulary. The method includes the use of a trigram language model on character sequences. Using all these techniques, we have achieved character error rates of 1.1 percent on data from the University of Washington English Document Image Database and 3.3 percent on data from the DARPA Arabic OCR Corpus     

Video OCR: indexing digital news libraries by recognition of superimposed captions

The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multi-frame integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.     

基于改进型CLAFIC学习子空间算法的有限汉字集识别

采用改进型ＣＬＡＦＩＣ（Ｃｌａｓｓ－Ｆｅａｔｕｒｉｎｇ Ｉｎｆｏｒｍａｔｉｏｎ Ｃｏｍｐｒｅｓｓｉｏｎ）算法可以为学习子空间ＬＳＭ（Ｌｅａｒｎｉｎｇ Ｓｕｂｓｐａｃｅ Ｍｅｔｈｏｄ）算法提供更好的初始向量子空间,并通过ＬＳＭ算法对各类样本子空间按不同的旋转方式训练,来提高ＯＣＲ的识别率,该文的特点在于首先采用了学习子空间算法来实现字符在灰度图像上的识别,它克服了传统的基于二值化图像进行特征提取和识     

Bayesian super-resolution of text in videowith a text-specific bimodal prior

To increase the range of sizes of video scene text recognizable by optical character recognition (OCR), we developed a Bayesian super-resolution algorithm that uses a text-specific bimodal prior. We evaluated the effectiveness of the bimodal prior, compared and in conjunction with a piecewise smoothness prior, visually and by measuring the accuracy of the OCR results on the variously super-resolved images. The bimodal prior improved the readability of 4- to 7-pixel-high scene text significantly better than bicubic interpolation and increased the accuracy of OCR results better than the piecewise smoothness prior.     

技术综合集成在模式识别中的应用

本文介绍了采用综合技术集成的方法，解决印刷汉字识别系统误识率太高的重大难题，并通过集成系统的实践，证实了其技术集成优势，由于识别方法的互补效应，不仅提高了识别的正确率，而且使误识率得到大幅度的降低，采用该集成办法研制的系统，经过１００万字的实际文章的测试，系统的识别率超过９８％，误识率小于０．２％，尤其是汉字的误识率小于０．１％。     

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.     

OCR和OMR同时存在的表格数据识别编程

提出一个通用的表格结构和模块 ,解决了 OCR和 OMR同时存在的表格数据的识别。其方法具有很大的实用性 ,大大地减轻了编程人员的工作负担