维汉英混排文档识别

维、汉、英是特点完全不同的文字。该文依据多层次语言判断和适当干预的多语言字符识别系统设计原则首次实现了维、汉、英混排文本识别系统。识别系统首先根据维、汉、英文字的各自特点实现字符块语言属性的初步判断,然后针对每种文字设计不同的字符切割算法。字符识别可信度用来判断字符语言属性和字符切分结果是否正确。实验结果表明,各种维、汉、英混排文本识别率达到96.4%以上。     

基于粗精交互融合和迭代图割的舰船可见光图像分割方法

在深入分析典型图割算法的基础上,提出了一种基于粗精交互融合和迭代图割的舰船可见光图像分割方法,主要包括矩形包围盒式的粗交互、基于高斯混合模型的迭代图割、多边形编辑式的精交互和窄带区域内的图割优化。仿真结果表明,本文方法基本达到了预期目标,交互少、分割效果好,可满足后续特征提取和目标识别的需求。     

Dedicated LSI for a Microprocessor-Controlled Hand-Carried OCR System

The binary picture processing and recognizing stages of an optical character recognition (OCR) system have been designed using both flexibility of available microprocessors and speed of peripheral custom-designed integrated circuits, A dedicated Iarge-scale integrated (LSI) processor performs edge detection and thinning of a 32 X 24 digitied one-piece pattern. The output signal-a set of 3 bit vectors describing the skeletonized character contour-feeds a microprocessor which controls the character recognition algorithm including pattern segmentation, filtering, feature extraction, and classification decision. This low-cost equipment is especirdly suitable for hand-carried OCR systems where well-formed printed alphanumerics are to be read: However, continously deformed patterns like carefully handprinted characters are recognized as well. A system reading speed of 100 characters/s (or 30 cm/s) can be achieved.     

A two-chip imaging system for OCR applications

Presents a two chip imaging system for optical character recognition (OCR) applications. It consists of a 64/spl times/24 photodiode array providing directly a binary pattern. The image quality is enhanced by means of a separate signal processing chip which performs centering of the image and delivers control signals for further on-line character recognition. The two chips are part of a fully integrated OCR wand.     

基于Hough变换的倾斜文本图像的检测

在OCR图像扫描输入的过程中,扫描图像经常会出现某种程度的倾斜,这种倾斜会给下一个字符的切割造成困难,影响字符识别的精度。正是出于检测倾斜文本图像角度的目的,提出了一种基于Hough变换的检测图像倾斜度的方法,可以有效地克服几何失真对文字识别系统的影响,为了克服Hough变换计算量大的缺点,该方法采用了提取图像特征点的方法。实验结果表明,该方法能快速准确地测出各类文本图像的倾斜角度,并且具有很好的适应性。     

A Novel Character Segmentation Method for Text Images Captured by Cameras

Due to the rapid development of mobile devices equipped with cameras, instant translation of any text seen in any context is possible. Mobile devices can serve as a translation tool by recognizing the texts presented in the captured scenes. Images captured by cameras will embed more external or unwanted effects which need not to be considered in traditional optical character recognition (OCR). In this paper, we segment a text image captured by mobile devices into individual single characters to facilitate OCR kernel processing. Before proceeding with character segmentation, text detection and text line construction need to be performed in advance. A novel character segmentation method which integrates touched character filters is employed on text images captured by cameras. In addition, periphery features are extracted from the segmented images of touched characters and fed as inputs to support vector machines to calculate the confident values. In our experiment, the accuracy rate of the proposed character segmentation system is 94.90%, which demonstrates the effectiveness of the proposed method.     

Knowledge model based approach in recognition of on-line Chinesecharacters

A knowledge model-based OCR system is presented for the recognition of on-line connected stroke Chinese characters. In the approach, segment attributes are first extracted to characterize the segment sequence of an unknown character. Next, radical recognition based on model matching is adopted as the coarse classification to reduce the number of candidate characters before detailed matching. Finally, a deviation modeling method is proposed to recognize not only regular writing characters but also characters with stroke-order and stroke-number deviations. The effectiveness of the approach is verified by experiments on the recognition of on-line Chinese characters     

基于Android的手机OCR识别技术设计与实现

介绍了OCR文字处理和识别的工作原理,以及OCR运用在手机平台的优越性。设计了基于Android的OCR中英文识别日程小秘书软件,重点对OCR识别软件的功能进行分析,并设计实现方案,最终完成了OCR日程提醒功能。经过对软件系统功能的测试,结果表明,OCR识别软件在功能上能完成日常的文字识别与提取。     

红外目标检测与识别技术研究

在图像分析和目标识别技术中,图像分割和特征提取是两个非常关键的问题。但是往往会由于对目标分割的不够准确和完整,导致特征提取变得很困难,首先采取自动多门限算法对图像初步分割,再利用二维直方图准确划分出目标,最后利用组合概率的方法将区域的分散度、不变矩进行综合,实验结果表明对五类目标的识别效果十分明显。     

A knowledge-based system controlled by an iterative quadtreesplitting scheme for segmentation of seismic sections

A knowledge-based system for the segmentation of seismic sections is presented. The system can be functionally divided into a texture feature extraction part and a knowledge-based segmentation part. An important characteristic of the proposed approach is the iterative quadtree splitting (IQS) scheme used to control the segmentation process. The final output of the system is a segmentation of the input section into regions (segments) of common signal character. Test runs of the system on a real seismic section from the Gulf of Mexico show that the introduction of domain expert geologic knowledge can significantly improve the overall segmentation. The IQS control scheme provides two functions essential to most knowledge-based image processing and interpretation systems: (1) the coordination of all parallel-operated processes over the entire section for an overall balanced result; and (2) the incorporation of various types of knowledge into the different levels of decision-making in those processes     

Neural network-based systems for handprint OCR applications

Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized.     

一种基于互相关匹配的简化PCNN及其图像分割

脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)可有效的用于图像分割,但无法确定最优分割时的迭代次数.针对这一问题,文章从原始图像与分割图像的相似性出发,提出了一种基于最大互相关匹配的简化PCNN图像分割新方法.该算法通过计算原始图像与分割图像的相关匹配系数来确定最优分割.实验结果验证了该方法的有效性.     

基于并行分离增广拉格朗日乘子法的字符矫正

字符矫正是光学字符识别(OCR)系统预处理过程中 的重要步骤,针对传统的增广拉格朗日乘子法(ALM)求解字符矫正问题时收敛性和计算速度 的不足,本文研究了并行分离的增广拉格朗日乘子法,综合考虑字符矫正模型的建立过程, 提出并行分离方法与ALM相结合的思想解决字符 矫正问题。用并行方式将迭代问题分解成3个子问题,计算时能够同时求解分解后的这3个 子问题,然后进行凸组合,最 后收敛到问题的最优解。实验结果表明,本文算法能够快速准确地对变形的字符图像进 行矫正,并且具有良好的实时性和适 应性,可用于OCR系统的矫正预处理中,提高OCR系统的识别率。     

Omnidocument technologies

An optical character recognition (OCR) engine that is omnifont and reasonably robust on individual degraded characters is presented. The weakest link is its handling of characters which are difficult to segment. The engine is divided into four phases: segmentation, image recognition, ambiguity resolution, and document analysis. The features are zonal and reduce the image to a blurred, gray-level representation. The classifier is data-driven, trained offline, and model-free. Handcrafted features and decision trees tend to be brittle in the presence of noise. To satisfy the needs of full-text applications, the system captures the structure of the document so that, when viewed in a word processor or spreadsheet program, the formatting of the optically recognized document reflects that of the original document. To satisfy the needs of the forms market, a proofing and correction tool displays `pop-up' images of uncertain characters     

一种低亮度非均匀光照文档图片快速二值化方法

二值化是光学文字识别(OCR)的重要步骤,直接影响到光学文字识别的成功率。目前基 于亮度分割局域二值化算法效果好,但是过程复杂、运算耗时。快速二值化算法流程简单、 噪声敏感。低亮度图片一般有不可忽略的噪声,并且文字对比度低。为获取低对比度文字, 快速二值化算法需对亮度梯度敏感,但是也会导致快速二值化结果文字断裂、丢失、背景噪 声大。为实现高质量快速二值化,本文采取非局域均值滤波算法抑制噪声,同时避免过度平 滑图片。采用改进的Bradley算法提取低对比度文字,并解决了文字断裂等问题。最后采用 膨胀腐蚀算法抑制二值化噪声。本方法适用于非均匀低亮度和高亮度的图片。实验结果表明 ,本方法在非均匀高亮度下,表现和其他快速二值化算法相同。在非均匀低亮度下,提取文 字更多、文字断裂更少、噪声更小。本方法二值化结果的OCR召回率达到了93.5%。     

Gradient-based learning applied to document recognition

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day     

An analog VLSI implementation of a feature extractor for real timeoptical character recognition

The architecture, the design, and the analog very large scale integration (VLSI) implementation of a feature extractor chip for optical character recognition (OCR) systems are described. The chip extracts a set of 112 feature values coded by current signals from a 32×24 digital pixel matrix, representing the input character. Such features are applied to a classifier (for example, a neural classifier) performing the recognition task. The measurements performed on that chip confirm its functionality. The chip can be used with a segmented and nonsegmented string of characters. A throughput of about 140 kChar/s is achieved for the segmented case, while a throughput of about 450 kChar/s is achieved for the nonsegmented case. The OCR architecture has been functionally validated. A set of numerical handwritten characters has been processed by the chip and the measured output features (after a normalization operation) have been used as input for neural network classifier; implemented by a software simulator which performs the recognition task. The resulting classification error rate (4.3%) has been successfully compared with those obtained by a high level model of this chip, and the results validate the entire architecture     

Variable duration hidden Markov model and morphologicalsegmentation for handwritten word recognition

This paper describes a complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden Markov model (CD-VDHMM). First, a new segmentation algorithm based on mathematical morphology is developed to translate the 2-D image into a 1-D sequence of subcharacter symbols. This sequence of symbols is modeled by the CDVDHMM. Thirty-five features are selected to represent the character symbols in the feature space. Generally, there are two information sources associated with written text; the shape information and the linguistic knowledge. While the shape information of each character symbol is modeled as a mixture Gaussian distribution, the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. The variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. A modified Viterbi algorithm, which provides l globally best paths, is adapted to VDHMM by incorporating the duration probabilities for the variable duration state sequence. The general string editing method is used at the postprocessing stage. The detailed experiments are carried out for two postal applications; and successful recognition results are reported.     

基于记忆分子动理论优化算法的多目标截面投影Otsu图像分割

传统截面投影Otsu法后处理过程中的阈值Q为预先设定的常量,对含噪程度不同的图像普适性较差。该文提出一种基于记忆分子动理论优化算法的多目标截面投影Otsu法。该方法将阈值Q作为变量,结合分割阈值T,基于最大类间方差和最大峰值信噪比准则建立多目标图像分割模型,以兼顾图像分割的准确性和抗噪性;为免阈值增加而影响算法效率,将人工记忆原理引入分子动理论优化算法,设计了一种基于记忆分子动理论优化算法的多目标图像分割模型求解方法。实验表明：该方法分割准确、抗噪性强、鲁棒性好,对含不同噪声的图像更具普适性。