Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.     

Text line extraction from multi-skewed handwritten documents

A novel text line extraction technique is presented for multi-skewed document images of handwritten English or Bengali text. It assumes that hypothetical water flows, from both left and right sides of the image frame, face obstruction from characters of text lines. The stripes of areas left unwetted on the image frame are finally labelled for extraction of text lines. The success rate of the technique, as observed experimentally, are 90.34% and 91.44% for handwritten Bengali and English document images, respectively. The work may contribute significantly for the development of applications related to optical character recognition of Bengali/English text.     

特征离散点计算在手写文本行分割中的应用

将图像分析实践中的经验知识与粒计算的基本思想相结合,总结形成了特征离散点计算,并将其应用于自然手写汉字文本行分割当中。在特征离散点计算的结构化问题求解框架下,提出了一种反馈式分列行投影文本行分割方法,分为特征离散点选择、特征离散点采样与优化、特征离散点编组与反馈以及行边缘优化四个环节。该方法在哈尔滨工业大学多人手写数据库上取得了相对以往算法较好的实验结果,同时分割速度较快。     

A new scheme for unconstrained handwritten text-line segmentation

Variations in inter-line gaps and skewed or curled text-lines are some of the challenging issues in segmentation of handwritten text-lines. Moreover, overlapping and touching text-lines that frequently appear in unconstrained handwritten text documents significantly increase segmentation complexities. In this paper, we propose a novel approach for unconstrained handwritten text-line segmentation. A new painting technique is employed to smear the foreground portion of the document image. The painting technique enhances the separability between the foreground and background portions enabling easy detection of text-lines. A dilation operation is employed on the foreground portion of the painted image to obtain a single component for each text-line. Thinning of the background portion of the dilated image and subsequently some trimming operations are performed to obtain a number of separating lines, called candidate line separators. By using the starting and ending points of the candidate line separators and analyzing the distances among them, related candidate line separators are connected to obtain segmented text-lines. Furthermore, the problems of overlapping and touching components are addressed using some novel techniques. We tested the proposed scheme on text-pages of English, French, German, Greek, Persian, Oriya and Bangla and remarkable results were obtained.     

Learning-based word spotting system for Arabic handwritten documents

The retrieval of information from scanned handwritten documents is becoming vital with the rapid increase of digitized documents, and word spotting systems have been developed to search for words within documents. These systems can be either template matching algorithms or learning based. This paper presents a coherent learning based Arabic handwritten word spotting system which can adapt to the nature of Arabic handwriting, which can have no clear boundaries between words. Consequently, the system recognizes Pieces of Arabic Words (PAWs), then re-constructs and spots words using language models. The proposed system produced promising result for Arabic handwritten word spotting when tested on the CENPARMI Arabic documents database.     

Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introduce the following innovative aspects: (i) use of a novel Adaptive Run Length Smoothing Algorithm (ARLSA) in order to face the problem of complex and dense document layout, (ii) detection of noisy areas and punctuation marks that are usual in historical machine-printed documents, (iii) detection of possible obstacles formed from background areas in order to separate neighboring text columns or text lines, and (iv) use of skeleton segmentation paths in order to isolate possible connected characters. Comparative experiments using several historical machine-printed documents prove the efficiency of the proposed technique.     

Text line segmentation of historical documents: a survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade and dedicated to documents of historical interest.     

笔迹图像中的单个汉字字符分割

提出了一套完整的针对单字的笔迹图像分割算法,选用不同的笔迹样本作了验证实验,对实现单字分割做了全面的阐述论证。将模板分割算法中的行分割、字分割、单字图像库建立和基于模板匹配的分割算法结合在一起,提高了算法的运算速度和精确度。利用50幅笔迹样本进行测试,92%的单字分割样本可以作为单字模板,应用模板匹配分割算法92%的样本可以实现单字提取。     

A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature.     

鲁棒的车辆行道线提取方法

提出了一种新的行道线提取方法。该方法利用均值滤波对道路图像进行亮度估计,把均值滤波后的图像和原图像进行差分从而突出白色的行道线区域,并且采用多阈值方案,对得到的差图像进行二值化。对得到的包含行道线的二值图像进行干扰去除和细化处理,并运用基于加权的Hough变换求得多条候选行道线,基于空间约束从所得的候选行道线中挑选出合适的直线对作为左右行道线。实验结果表明,该算法在复杂路况的情况下能够快速准确地提取出行道线,从而实现对驾车者偏离车道的报警。     

Handwritten document image segmentation into text lines and words

Two novel approaches to extract text lines and words from handwritten document are presented. The line segmentation algorithm is based on locating the optimal succession of text and gap areas within vertical zones by applying Viterbi algorithm. Then, a text-line separator drawing technique is applied and finally the connected components are assigned to text lines. Word segmentation is based on a gap metric that exploits the objective function of a soft-margin linear SVM that separates successive connected components. The algorithms tested on the benchmarking datasets of ICDAR07 handwriting segmentation contest and outperformed the participating algorithms.     

A robust approach to text line grouping in online handwritten Japanese documents

In this paper, we present an effective approach for grouping text lines in online handwritten Japanese documents by combining temporal and spatial information. With decision functions optimized by supervised learning, the approach has few artificial parameters and utilizes little prior knowledge. First, the strokes in the document are grouped into text line strings according to off-stroke distances. Each text line string, which may contain multiple lines, is segmented by optimizing a cost function trained by the minimum classification error (MCE) method. At the temporal merge stage, over-segmented text lines (caused by stroke classification errors) are merged with a support vector machine (SVM) classifier for making merge/non-merge decisions. Last, a spatial merge module corrects the segmentation errors caused by delayed strokes. Misclassified text/non-text strokes (stroke type classification precedes text line grouping) can be corrected at the temporal merge stage. To evaluate the performance of text line grouping, we provide a set of performance metrics for evaluating from multiple aspects. In experiments on a large number of free form documents in the Tokyo University of Agriculture and Technology (TUAT) Kondate database, the proposed approach achieves the entity detection metric (EDM) rate of 0.8992 and the edit-distance rate (EDR) of 0.1114. For grouping of pure text strokes, the performance reaches EDM of 0.9591 and EDR of 0.0669.     

Mixture model for face-color modeling and segmentation

In this paper, we propose a general methodology for face-color modeling and segmentation. One of the major difficulties in face detection and retrieval is partial face extraction due to highlights, shadows and lighting variations. We show that a mixture-of-Gaussians modeling of the color space, provides a robust representation that can accommodate large color variations, as well as highlights and shadows. Our method enables to segment within-face regions, and associate semantic meaning to them, and provides statistical analysis and evaluation of the dominant variability within a given archive.     

Text extraction in complex color documents

Text extraction in mixed-type documents is a pre-processing and necessary stage for many document applications. In mixed-type color documents, text, drawings and graphics appear with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. In this paper, a new method to automatically detect and extract text in mixed-type color documents is presented. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors and to convert the document into the principal of them. Then, using the principal colors, the document image is split into the separable color plains. Thus, binary images are obtained, each one corresponding to a principal color. The PLA technique is applied independently to each of the color plains and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color plains and to produce the final document. Several experimental and comparative results, exhibiting the performance of the proposed technique, are also presented.     

HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components

In off-line handwriting recognition, classifiers based on hidden Markov models (HMMs) have become very popular. However, while there exist well-established training algorithms which optimize the transition and output probabilities of a given HMM architecture, the architecture itself, and in particular the number of states, must be chosen “by hand”. Also the number of training iterations and the output distributions need to be defined by the system designer. In this paper we examine several optimization strategies for an HMM classifier that works with continuous feature values. The proposed optimization strategies are evaluated in the context of a handwritten word recognition task.     

区域GMM聚类的SAR图像分割

高斯混合模型(GMM)聚类算法近年来广泛应用于图像分割领域。但在SAR图像分割中,由于忽略了图像像素间的空间相关性,使其对相干斑噪声十分敏感。提出一种基于区域的GMM聚类算法,它将空间相关性引入聚类分类中,利用分水岭分割得到基本同质区域,计算区域的灰度均值作为GMM聚类算法的输入样本,将聚类特征从像素水平提升到区域水平,减少了噪声对分割结果的影响;并将自身反馈机制引入期望最大化(EM)算法中,进一步提高了GMM模型参数估计的精度。还对合成图像和真实SAR图像进行了分割实验,结果表明新算法可有效地提高分割的     

图像中多语种文本提取的高斯混合建模方法

建立了相邻字符区域的高斯混合模型,用于区分字符与非字符.在此基础上,提出了一种从图像中提取多语种文本的方法.首先对输入图像进行二值化,并执行形态学闭运算,使二值图像中每个字符成为一个单独的连通成分.然后根据各连通成分重心的Voronoi区域,形成连通成分之间的邻接关系;最后在贝叶斯框架下,基于相邻字符区域的高斯混合模型计算相应的伪概率,以此为判据将每个连通成分标注为字符或非字符.利用所提出的文本提取方法,进行了复杂中英文文本的提取实验,获得大于97%的准确率和大于80%的召回率,证实了方法的有效性.     

A differential-processing extraction approach to text and image segmentation

To efficiently store the information found in paper documents, text and non-text regions need to be separated. Non-text regions include half-tone photographs and line diagrams. The text regions can be converted (via an optical character reader) to a computer-searchable form, and the non-text regions can be extracted and preserved in compressed form using image-compression algorithms. In this paper, an effective system for automatically segmenting a document image into regions of text and non-text is proposed. The system first performs an adaptive thresholding to obtain a binarized image. Subsequently the binarized image is smeared using a run-length differential algorithm. The smeared image is then subjected to a text characteristic filter to remove error smearing of non-text regions. Next, baseline cumulative blocking is used to rectangularize the smeared region. Finally, a text block growing algorithm is used to block out a text sentence. The recognition of text is carried out on a text sentence basis.     

Keyword-guided word spotting in historical printed documents using synthetic data and user feedback

In this paper, we propose a novel technique for word spotting in historical printed documents combining synthetic data and user feedback. Our aim is to search for keywords typed by the user in a large collection of digitized printed historical documents. The proposed method consists of the following stages: (1) creation of synthetic image words; (2) word segmentation using dynamic parameters; (3) efficient feature extraction for each word image and (4) a retrieval procedure that is optimized by user feedback. Experimental results prove the efficiency of the proposed approach.