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.     

Adaptive binarization method for degraded document images based on surface contrast variation

Document binarization is an important technique in document image analysis and recognition. Generally, binarization methods are ineffective for degraded images. Several binarization methods have been proposed; however, none of them are effective for historical and degraded document images. In this paper, a new binarization method is proposed for degraded document images. The proposed method based on the variance between pixel contrast, it consists of four stages: pre-processing, geometrical feature extraction, feature selection, and post-processing. The proposed method was evaluated based on several visual and statistical experiments. The experiments were conducted using five International Document Image Binarization Contest benchmark datasets specialized for binarization testing. The results compared with five adaptive binarization methods: Niblack, Sauvola thresholding, Sauvola compound algorithm, NICK, and Bataineh. The results show that the proposed method performs better than other methods in all binarization cases.     

Direct gray-scale extraction of features for character recognition

A method for feature extraction directly from gray-scale images of scanned documents without the usual step of binarization is presented. This approach eliminates binarization by extracting features directly from gray-scale images. In this method, a digitized gray-scale image is treated as a noisy sampling of the underlying continuous surface and desired features are obtained by extracting and assembling topographic characteristics of this surface. The advantages and effectiveness of the approach are both shown theoretically and demonstrated through preliminary experiments of the proposed method     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Document binarization with automatic parameter tuning

Document analysis systems often begin with binarization as a first processing stage. Although numerous techniques for binarization have been proposed, the results produced can vary in quality and often prove sensitive to the settings of one or more control parameters. This paper examines a promising approach to binarization based upon simple principles, and shows that its success depends most significantly upon the values of two key parameters. It further describes an automatic technique for setting these parameters in a manner that tunes them to the individual image, yielding a final binarization algorithm that can cut total error by one-third with respect to the baseline version. The results of this method advance the state of the art on recent benchmarks.     

An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction

This paper propose a computerized method of magnetic resonance imaging (MRI) of brain binarization for the uses of preprocessing of features extraction and brain abnormality identification. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to extensive black background or large variation in contrast between background and foreground of MRI. We have proposed a binarization that uses mean, variance, standard deviation and entropy to determine a threshold value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.     

VCPPS:一种面向并行Verilog模拟的代码分割器

本文介绍了一种面向并行模拟的Verilog代码分割器VCPPS。VCPPS通过图形化的方式进行人机交互,并采取用户启发式的方法指导分割。文章介绍了VCPPS设计与实现中的一些关键技术,通过分析可以看出,VCPPS可以正确地完成繁琐的Verilog代码分割工作,为并行Verilog模拟提供支持。     

基于Gabor小波的视网膜血管自动提取研究

针对视网膜血管网络灰度分布特征和区域结构特征,提出了一种基于Gabor小波的视网膜血管提取方法。采用Gabor滤波预处理以增强血管,用改进的自适应二值化方法对增强后的视网膜图像进行二值化处理,根据视网膜血管具有区域连通性的特征,并用形态学方法分割出最终的血管。为验证方法的有效性,对Hoover眼底图像库进行实验,结果表明该方法在细小血管的提取以及连续性、有效性方面都优于Hoover算法。     

A learning framework for the optimization and automation of document binarization methods

Almost all binarization methods have a few parameters that require setting. However, they do not usually achieve their upper-bound performance unless the parameters are individually set and optimized for each input document image. In this work, a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image. The framework, which works with any binarization method, has a standard structure, and performs three main steps: (i) extracts features, (ii) estimates optimal parameters, and (iii) learns the relationship between features and optimal parameters. First, an approach is proposed to generate numerical feature vectors from 2D data. The statistics of various maps are extracted and then combined into a final feature vector, in a nonlinear way. The optimal behavior is learned using support vector regression (SVR). Although the framework works with any binarization method, two methods are considered as typical examples in this work: the grid-based Sauvola method, and Lu’s method, which placed first in the DIBCO’09 contest. The experiments are performed on the DIBCO’09 and H-DIBCO’10 datasets, and combinations of these datasets with promising results.     

时空混沌二值化方法研究

针对耦合映像格子模型产生的时空混沌序列的特点,在符号函数二值化法的基础上,给出了一种新的二值化方法。随机性能和相关性检验的结果表明按这种方法得到的二值序列具有优良的伪随机性能及相关特性,说明提出的方法是有效的,而且简单实用。     

一种区域收缩及分类的车牌二值化方法

对车牌区域进行二值化一直是车牌识别系统的一个关键问题。针对车牌区域的特征,提出了一种基于分类思想的二值化方法。该算法从统计判别分析的思想出发,将二值化问题看成是一个分类问题。首先对区域进行收缩取样,然后进行分类。为了提高二值化精度,其中还使用了迭代分类技术。另外为了评价车牌二值化效果,从车牌二值化应用角度出发提出了粘连度、字符断裂度、噪声颗粒数、运行消耗时间的指标体系,用来评价车牌二值化的效果。有了这套指标体系,就可以方便地对各种车牌二值化技术进行评价。实验结果表明,该二值化算法简单有效。     

弱边缘电荷耦合器件羊毛图像二值化算法

为解决弱边缘图像二值化产生羊毛几何尺寸失真问题,通过对基于灰度和梯度指数的边缘细化算法研究,结合经典的全局阈值法和局部阈值法,提出了一种电荷耦合器件（CCD）羊毛图像自动二值化算法。该算法将sobel算子和斜坡边缘模型引入现有边缘细化算法中,既增加寻找边缘点环节又改进灰度调整因子,达到提高处理效率和避免人为干预的目的;在分析最大类间方差法和Bernsen法的基础上,结合全局和局部阈值处理各个子图像,从而强化边缘细节,降低失真度。实验结果表明,与传统方法相比,该算法对于弱边缘图像二值化具有良好的性能。     

Intelligent region-based thresholding for color document images with highlighted regions

The study applies an intelligent region-based thresholding method for the binarization of color document images with highlighted regions. The results also indicate that the proposed method can threshold simultaneously when the background is gradually changing, reversed, or inseparable from the foreground, with efficient binarization results. Rather than the traditional method of scanning the entire document at least once, this method intelligently divides a document image into several foreground regions and decides the background range for each foreground region, in order to effectively process the detected document regions. Experimental results demonstrate the high effectiveness of the proposed method in providing promising binarization results with low computational cost. Furthermore, the results of the proposed method are more accurate than global, region-based, local, and hybrid methods. Images were analyzed using MODI OCR measurement data such as recall rate and precision rate. In particular, when test images produced under inadequate illumination are processed using the proposed method, the binarization results of this method have better visual quality and better measurable OCR performance than compared global, region-based, local, and hybrid methods. Moreover, the proposed algorithm can be run in an embedded system due to its simplicity and efficiency.     

一种基于全局均值和局部方差的图像二值化方法

比较了几种常用的动态阈值二值化方法，并在此基础上提出了一种新的基于均值和方差的二：值化方法。试验表明，这种方法能够较好地区分物体和背景，并且相对于原来的逐点进行二值化的方法而言大大提高了处理速度。     

Hilbert填充曲线与空间分布模式探测的点数据集空间划分方法

空间数据划分是空间索引、并行GIS数据分解以及分布式数据管理与调度等问题的核心环节之一。针对点数据集多目标空间划分问题,引入Hilbert空间填充曲线和空间分布模式探测过程,提出针对规则、随机和聚集分布模式的点数据集空间划分方法。实验结果表明,该方法能够在缺少覆盖范围信息的条件下准确判定空间分布类型,该方法能够兼顾空间聚集性、数据量均衡与空间重叠度3种约束条件。     

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.     

A binarization method with learning-built rules for document images produced by cameras

In this paper, we propose a novel binarization method for document images produced by cameras. Such images often have varying degrees of brightness and require more careful treatment than merely applying a statistical method to obtain a threshold value. To resolve the problem, the proposed method divides an image into several regions and decides how to binarize each region. The decision rules are derived from a learning process that takes training images as input. Tests on images produced under normal and inadequate illumination conditions show that our method yields better visual quality and better OCR performance than three global binarization methods and four locally adaptive binarization methods.     

A comparison study of evolutive algorithms for solving the partitioning problem in distributed virtual environment systems

Fast Internet connections and the widespread use of high performance graphic cards are making Distributed Virtual Environment (DVE) systems very common nowadays. However, there are several key issues in these systems that should still be improved in order to design a scalable and cost-effective system. One of these key issues is the partitioning problem. This problem consists of efficiently assigning clients (3D avatars) to the servers in the system. In this paper, we present a comparison study of different modern heuristics for solving the partitioning problem in DVE systems, as an alternative to the ad hoc heuristic proposed in the literature. Performance evaluation results show that some of the heuristic methods can greatly improve the performance of the partitioning method, particularly for large DVE systems. In this way, efficiency and scalability of DVE systems can be significantly improved.     

改进的多阈值动态二值化算法

在模式识别中，二值化效果的好坏直接影响着识别率。文章在研究现有二值化算法的基础上，对多阈值动态二值化进行了改进，并以身份证为例来验证该算法的有效性，实验结果表明所提算法有较高的抗噪音能力。