CN108830857A - A kind of adaptive Chinese character rubbings image binaryzation partitioning algorithm - Google Patents
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Abstract
本发明公开了一种自适应的汉字碑帖图像二值化分割算法,该方法分为四个步骤:(1)采用中值滤波预处理;(2)提取红色成分;(3)形态学运算以寻找最佳背景估计;(4)Otsu分割二值图像。本发明属于汉字碑帖图像分割技术领域,保留汉字的笔画特征同时增强字符细节,针对退化的历史碑帖图像,本发明提出了一种基于背景估计的非均匀光照退化的图像二值化自适应分割算法,所提出的方法的新颖之处在于找到一个基于盲/无参考图像空间质量评估的最佳背景估计,实验结果表明,该方法能够对退化汉字进行更精确的字符分割。
The invention discloses an adaptive binarization segmentation algorithm for images of inscriptions with Chinese characters. The method is divided into four steps: (1) preprocessing by median filtering; (2) extracting red components; (3) morphological operations with Find the best background estimate; (4) Otsu segment the binary image. The invention belongs to the technical field of image segmentation of Chinese character inscriptions, which preserves the stroke features of Chinese characters and enhances the details of characters. Aiming at degraded images of historical inscriptions, the invention proposes an image binarization adaptive segmentation algorithm based on background estimation and non-uniform illumination degradation , the novelty of the proposed method lies in finding an optimal background estimate based on blind/no-reference image spatial quality assessment, and experimental results show that the method is capable of more accurate character segmentation for degenerated Chinese characters.
Description
技术领域technical field
本发明属于汉字碑帖图像分割技术领域,具体是指一种自适应的汉字碑帖图像二值化分割算法。The invention belongs to the technical field of image segmentation of Chinese-character inscriptions, and specifically refers to an adaptive binary image segmentation algorithm for Chinese-character inscriptions.
背景技术Background technique
中国保留自己历史文化的重要方式是写在石头上的记忆——铭文。同时,拓片是中国古代书籍的重要组成部分,它是人们学习和研究历史的主要来源。拓片文献数字化是弘扬和传承中国传统艺术的新途径,是保护石质文物的新思路。另一方面,现存古籍拓片往往失去视觉质量。随着时间的推移,由于其保存条件、潮湿、污染等诸多因素的影响,在拓片复制成为数字图像后,由于对比度不同、噪声大、背景强度增强等原因,前景和背景很难被分开。除了低对比度、背景噪声往往还引入纹理石外,还有纸质材料的老化,因此,从古碑帖原图像中提取干净的汉字是一个具有挑战性的工作。但上述也是进行任何进一步的自动文档图像分析如版面分析、字符识别等的关键步骤等。拓片图像采集过程中的亮度分布不均匀,会影响图像的质量。An important way for China to preserve its own history and culture is the memory written on stones - inscriptions. At the same time, rubbings are an important part of ancient Chinese books, and they are the main source for people to learn and research history. The digitization of rubbing documents is a new way to promote and inherit Chinese traditional art, and a new way of thinking to protect stone cultural relics. On the other hand, rubbings of extant ancient books often lose their visual quality. With the passage of time, due to the influence of many factors such as its storage conditions, humidity, and pollution, after the rubbings are copied into digital images, it is difficult to separate the foreground and background due to reasons such as different contrast, high noise, and enhanced background intensity. In addition to low contrast, background noise often introduced textured stone, and the aging of paper materials, it is a challenging task to extract clean Chinese characters from the original image of ancient rubbings. But the above is also a key step for any further automatic document image analysis, such as layout analysis, character recognition, etc. The brightness distribution in the process of rubbing image acquisition is uneven, which will affect the quality of the image.
为了提高字符切分的质量,发明人研究了大量的方法和技术,其中最重要的预处理步骤是文本二值化,将文档图像从灰度或彩色图像转换成二值图像,在背景信息由白色像素表示、前景由黑色像素表示的基础上,分离古代文档图像的前景和背景。一种最简单且高效的图像处理技术可以用来分离文档图像的前景和背景层,就是阈值化,许多可归为全局和局部阈值算法的阈值技术,多阈值方法和自适应阈值技术。当图像在背景和前景上具有相同的对比度时,全局阈值是首选的,它们的光照均匀,目标和背景相差很大。局部自适应阈值用于恢复文档图像中的前景像素。一般来说,对退化碑帖图像选择一个算法是一个非常困难的过程。由于存在复杂的退化,许多实验结果表明,传统的弱目标图像光照均匀处理方法存在着目标与背景分离不完整或目标丢失、处理效率低等缺点。因此发明人提出了一种新的自适应算法来处理拓片图像,即使用盲/无参考图像空间质量评估利用自然图像统计(NSS)模型框架局部归一化亮度系数,利用模型参数量化的“非自然”。为了正确分割低对比度彩色图像,采用形态学Top-Hat算子与圆盘形结构元素和自适应像素,利用拓片结构之间的差异的红色分量。为了降低噪声,将中值滤波应用于阴影校正图像。In order to improve the quality of character segmentation, the inventors have studied a large number of methods and technologies, among which the most important preprocessing step is text binarization, which converts the document image from a grayscale or color image to a binary image. The foreground and background of ancient document images are separated on the basis that white pixels represent the foreground and the foreground is represented by black pixels. One of the simplest and most efficient image processing techniques that can be used to separate the foreground and background layers of a document image is thresholding, a number of thresholding techniques that can be classified as global and local thresholding algorithms, multi-thresholding methods, and adaptive thresholding techniques. Global thresholding is preferred when images have the same contrast on the background and foreground, they are evenly illuminated, and the target and background differ greatly. Locally adaptive thresholding is used to recover foreground pixels in document images. In general, choosing an algorithm for degraded inscription images is a very difficult process. Due to the existence of complex degradation, many experimental results show that the traditional method of uniform illumination processing of weak target images has disadvantages such as incomplete separation of target and background or loss of target, and low processing efficiency. Therefore, the inventors proposed a new adaptive algorithm to process rubbing images, that is, using blind/no-reference image spatial quality assessment, using the Natural Image Statistics (NSS) model framework to locally normalize the brightness coefficient, and using the "non- nature". To correctly segment low-contrast color images, a morphological Top-Hat operator with disk-shaped structural elements and adaptive pixels is employed to exploit the red component of the difference between rubbing structures. To reduce noise, median filtering is applied to the shading corrected image.
一般来说,退化的文档图像是由于背景噪声以及对比度和亮度的变异产生的。阴影退化的文档图像更常见,因为相机文档更容易受到光照变化的影响。现有很多算法,试图在扫描文本文档时分割前景和背景,但阈值是某种形式和另一区域的标准的工具,如Bernsen的自适应阈值是根据每个像素的领域来估计。使用局部最大值和最小值来构建局部对比度图像。然后,在该图像上应用滑动窗口来确定局部阈值。现有技术提出了一种基于相位来分割古代文档图像二值化模型,开发了一个真实的生成工具,称为PhaseGT,来简化和加快真正古代文档图像的生成过程。最近,现有技术中还提出了一种主动轮廓演化算法,根据文档图像的内在几何测量、图像对比度,即由图像的局部最大值和最小值,用于自动生成我们的活动轮廓模型,初始化图像;最后,平均阈值也可以产生并最终二值化。正如发明人所观察到的,大多数二值化方法是基于对字符和背景之间灰度等级的直观观察,而不管退化的文档图像的自适应选择阈值。为了克服这些困难,本方案提出了一种自适应的方法,应用不同的方法从退化的文档图像来分割字符。In general, degraded document images are due to background noise and variations in contrast and brightness. Document images with degraded shadows are more common because camera documents are more susceptible to lighting changes. There are many existing algorithms that try to segment the foreground and background when scanning a text document, but thresholding is of one form or another and standard tools for another region, such as Bernsen's Adaptive Thresholding is estimated based on the field of each pixel. Use local maxima and minima to construct a local contrast image. Then, a sliding window is applied on this image to determine a local threshold. The prior art proposes a phase-based binarization model for segmenting ancient document images, and develops a real generation tool called PhaseGT to simplify and speed up the process of generating real ancient document images. Recently, an active contour evolution algorithm has also been proposed in the prior art, which is used to automatically generate our active contour model based on the intrinsic geometric measures of the document image, image contrast, i.e., by local maxima and minima of the image, initializing the image ; Finally, an average threshold can also be generated and finally binarized. As observed by the inventors, most binarization methods are based on intuitive observation of gray levels between characters and background, regardless of adaptive selection thresholds for degraded document images. To overcome these difficulties, this scheme proposes an adaptive method that applies different methods to segment characters from degraded document images.
发明内容Contents of the invention
为解决上述现有难题,保留汉字的笔画特征同时增强字符细节,针对退化的历史碑帖图像,本发明提出了一种基于背景估计的非均匀光照退化的图像二值化自适应分割算法,所提出的方法的新颖之处在于找到一个基于盲/无参考图像空间质量评估的最佳背景估计,该方法分为四个步骤:(1)采用中值滤波预处理; (2)提取红色成分;(3)形态学运算以寻找最佳背景估计;(4)Otsu分割二值图像。实验结果表明,该方法能够对退化汉字进行更精确的字符分割。In order to solve the above existing problems and preserve the stroke features of Chinese characters while enhancing the character details, the present invention proposes an image binarization adaptive segmentation algorithm based on background estimation and non-uniform illumination degradation for degraded historical rubbing images. The novelty of the method is to find an optimal background estimate based on blind/no-reference image spatial quality assessment, which is divided into four steps: (1) preprocessing with median filtering; (2) extraction of red components; ( 3) Morphological operation to find the best background estimation; (4) Otsu segment binary image. Experimental results show that this method can perform more accurate character segmentation on degenerate Chinese characters.
本发明采用的技术方案如下:一种自适应的汉字碑帖图像二值化分割算法,使用彩色图像开发了一种鲁棒算法,用于从背景中分割中文拓片图像,包括如下步骤:The technical scheme that the present invention adopts is as follows: a kind of self-adaptive Chinese character stele post image binarization segmentation algorithm, uses color image to develop a kind of robust algorithm, is used for segmenting Chinese rubbing image from background, comprises the following steps:
1)使用中值滤波重新处理,中值滤波允许大量高空间频率细节通过,同时非常有效地消除平滑邻域中小于一半像素的图像上的噪声;1) Reprocessing using median filtering, which allows a lot of high spatial frequency detail to pass through while being very effective at removing noise on images that are less than half the pixels in smooth neighborhoods;
2)提取红色成分;2) Extract the red component;
3)形态学图像处理操作,以便如果发现最小BRISQUE,则可以找出盘的最佳直径Thr*;3) Morphological image processing operations so that if a minimum BRISQUE is found, the optimal diameter Thr* of the disc can be found;
4)使用Otsu的分割二值图像。4) Use Otsu's to segment the binary image.
进一步地,步骤3)所述的形态学为数学形态学,利用数学形态学进行图像处理的基本思想是利用具有一定形状的结构元素(具有一定结构形状的基本元素,如矩形、圆形或菱形等)来检测目标图像,通过对图像目标区域和填充方法中结构元素的有效性进行分析,得到图像形态和结构的相关信息,并利用它们实现图像分析和识别的目的。Further, the morphology described in step 3) is mathematical morphology, and the basic idea of using mathematical morphology for image processing is to use structural elements with a certain shape (basic elements with a certain structural shape, such as rectangles, circles or rhombuses) etc.) to detect the target image, by analyzing the target area of the image and the effectiveness of the structural elements in the filling method, the relevant information about the shape and structure of the image is obtained, and they are used to achieve the purpose of image analysis and recognition.
进一步地,所述结构元素是形态学图像处理的一个关键点,不同的结构元素决定了图像中各种几何信息的分析和处理,也决定了数据转换过程中的计算量,因此对结构元素的分析是图像边缘检测的重要内容;结构元素的大小和结构形状都会影响图像边缘检测;小尺寸结构元素具有较弱的去噪能力,但它们可以检测精确的边缘细节;大尺寸结构元素具有更强的去噪能力,但检测到的边缘更加粗糙;更重要的是,不同形状的结构元素对不同图像的边缘有不同的处理能力;其中,灰度图像可以看作是一组二维点,膨胀和腐蚀操作可以表示如下:Furthermore, the structural elements are a key point in morphological image processing. Different structural elements determine the analysis and processing of various geometric information in the image, and also determine the amount of calculation in the data conversion process. Therefore, the structural elements Analysis is an important part of image edge detection; the size and shape of structural elements will affect image edge detection; small-sized structural elements have weak denoising ability, but they can detect precise edge details; large-sized structural elements have stronger The denoising ability, but the detected edge is rougher; more importantly, the structural elements of different shapes have different processing capabilities for the edges of different images; among them, the grayscale image can be regarded as a set of two-dimensional points, dilation and the corrosion operation can be expressed as follows:
Top-hat算法可以根据开放操作和闭合操作的不同组件分为Top-hat算法和Bot-hat算法,将Top-hat算法应用于图像并表示为TH:The Top-hat algorithm can be divided into Top-hat algorithm and Bot-hat algorithm according to the different components of the opening operation and the closing operation. The Top-hat algorithm is applied to the image and expressed as TH:
将Top-hat算法应用于图像并表示为BH:Apply the Top-hat algorithm to the image and denote it as BH:
BH(x,y)=(f⊙S-f)(x,y) (4)BH(x,y)=(f⊙S-f)(x,y) (4)
在方程中,f(x,y)是原始灰度图像,S(x,y)是结构元素,Top-hat变换通过原始图像与其打开操作之间的差异来提取前景信息,而Bot-hat变换通过原始图像与其闭合操作之间的差异来抑制背景信息。In the equation, f(x, y) is the original grayscale image, S(x, y) is the structural element, the Top-hat transformation extracts the foreground information by the difference between the original image and its opening operation, and the Bot-hat transformation Background information is suppressed by the difference between the original image and its closing operation.
进一步地,步骤3)所述的BRISQUE是一种基于空间图像统计特征的通用无参考图像质量评估算法,该算法基于以下理论前提:自然图像具有一定的规律性,人眼的视觉特征随着规律演变,在现有技术中, Anish Mittal和其他研究人员发现,空间域中自然图像的归一化亮度系数具有统计特性并符合单位高斯分布。此功能受图像失真的影响,不同的失真对分配有不同的影响。基于以上研究成果,我们提出了一种基于空间域统计特征的BRISQUE无参考图像质量评估算法。对于给定的尺寸为M*N的灰度图像,每个像素的亮度归一化系数满足如下:Further, the BRISQUE described in step 3) is a general-purpose no-reference image quality assessment algorithm based on spatial image statistical features, which is based on the following theoretical premise: natural images have certain regularity, and the visual features of human eyes follow the regularity Evolution, In the prior art, Anish Mittal and other researchers found that the normalized luminance coefficients of natural images in the spatial domain have statistical properties and follow a unit Gaussian distribution. This function is affected by image distortion, and different distortions have different effects on the distribution. Based on the above research results, we propose a BRISQUE no-reference image quality assessment algorithm based on spatial domain statistical features. For a given grayscale image of size M*N, the brightness normalization coefficient of each pixel satisfies the following:
式中:i=1,2,…,M;j=1,2,…,N;c是常数,c=1;K=L=3;i M=…1,2,,;j N=…1,2,,;c is a constant,c=1;K=L=3;μ(i,j)和σ(i,j)是平均值和标准差;ω={ω}kj∣k=-K,-K+1,…,K,L=-L, -L+1,…,L是二维高斯方程的采样和标准化;BRISQUE算法使用亮度归一化系数作为质量相关特征来评估图像质量。与其他非参考质量评估相比,图像特征的使用消除了对各种复杂变换的需求。因此,该算法在精度相近的前提下具有计算简单,节省时间的优点。另一方面,图像亮度的去相关处理忽略了亮度对测试图像质量的影响。In the formula: i=1,2,...,M; j=1,2,...,N; c is a constant, c=1; K=L=3; i M=...1,2,,; j N= …1,2,,; c is a constant, c=1; K=L=3; μ(i,j) and σ(i,j) are mean and standard deviation; ω={ω} kj ∣k = -K, -K+1, ..., K, L = -L, -L+1, ..., L is the sampling and normalization of the two-dimensional Gaussian equation; the BRISQUE algorithm uses the brightness normalization coefficient as a quality-related feature to evaluate Image Quality. In contrast to other non-reference quality assessments, the use of image features eliminates the need for various complex transformations. Therefore, the algorithm has the advantages of simple calculation and time saving under the premise of similar accuracy. On the other hand, the decorrelation process of image brightness ignores the effect of brightness on the quality of the test image.
进一步地,步骤4)使用Otsu的分割二值图像中包括二值化分割算法,二值化分割算法中包括Jaccard 系数、假阳性率FPR和假阴性率FNR三个参数进行分层测量,假阳性率FPR显示欠分割程度,假阴性率 FNR显示过度分割程度;Jaccard系数测量有限样本集之间的相似性,并且定义为相交的大小除以样本集的并集的大小,对于二值图像,计算二值图像A和B的交除以A和B的并,Jaccard系数可以使用以下公式计算:Further, step 4) uses Otsu's segmentation binary image to include a binarization segmentation algorithm, including Jaccard coefficient, false positive rate FPR and false negative rate FNR three parameters in the binary segmentation algorithm for hierarchical measurement, false positive The rate FPR shows the degree of under-segmentation, and the false-negative rate FNR shows the degree of over-segmentation; the Jaccard coefficient measures the similarity between finite sample sets and is defined as the size of the intersection divided by the size of the union of the sample sets. For binary images, calculate The intersection of binary images A and B is divided by the union of A and B, and the Jaccard coefficient can be calculated using the following formula:
假阳性率FPR和假阴性率FNR定义如下:The false positive rate FPR and false negative rate FNR are defined as follows:
其中FP是误报的数量,真实图像中的白色和二值化图像中的黑色,FN是假阴性的数量,TN是真阴性的数量,N=FP+TN是阴性的总数量。where FP is the number of false positives, white in real images and black in binarized images, FN is the number of false negatives, TN is the number of true negatives, and N=FP+TN is the total number of negatives.
采用上述方案本发明取得有益效果如下:本发明保留汉字的笔画特征同时增强字符细节,针对退化的历史碑帖图像,提出了一种基于背景估计的非均匀光照退化的图像二值化自适应分割算法,所提出的方法的新颖之处在于找到一个基于盲/无参考图像空间质量评估的最佳背景估计,该方法分为四个步骤:(1) 采用中值滤波预处理;(2)提取红色成分;(3)形态学运算以寻找最佳背景估计;(4)Otsu分割二值图像。实验结果表明,该方法能够对退化汉字进行更精确的字符分割。By adopting the above scheme, the present invention achieves the following beneficial effects: the present invention retains the stroke features of Chinese characters while enhancing character details, and proposes an image binarization adaptive segmentation algorithm based on background estimation and non-uniform illumination degradation for degraded historical rubbing images , the novelty of the proposed method lies in finding an optimal background estimate based on blind/no-reference image spatial quality assessment. The method is divided into four steps: (1) Preprocessing with median filtering; (2) Extracting red components; (3) morphological operations to find the best background estimate; (4) Otsu segmentation of binary images. Experimental results show that this method can perform more accurate character segmentation on degenerate Chinese characters.
附图说明Description of drawings
图1为本发明方法流程图;Fig. 1 is a flow chart of the method of the present invention;
图2为常规中国碑帖图像的拓片图像;Fig. 2 is the rubbing image of conventional Chinese tablet image;
图3是图2的直方图;Fig. 3 is the histogram of Fig. 2;
图4是图2的目标函数值图;Fig. 4 is the objective function value diagram of Fig. 2;
图5是图像分割结果对照图。Figure 5 is a comparison chart of image segmentation results.
具体实施方式Detailed ways
下面将对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅是本发明的一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例,都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
实施例,本实施例一种自适应的汉字碑帖图像二值化分割算法,使用彩色图像开发了一种鲁棒算法,用于从背景中分割中文拓片图像,包括如下步骤:Embodiment, a kind of self-adaptive binarization segmentation algorithm of Chinese character inscription post image in this embodiment, uses color image to develop a kind of robust algorithm, is used for segmenting Chinese rubbing image from background, comprises the following steps:
1)使用中值滤波重新处理,中值滤波允许大量高空间频率细节通过,同时非常有效地消除平滑邻域中小于一半像素的图像上的噪声;1) Reprocessing using median filtering, which allows a lot of high spatial frequency detail to pass through while being very effective at removing noise on images that are less than half the pixels in smooth neighborhoods;
2)提取红色成分;2) Extract the red component;
3)形态学图像处理操作,以便如果发现最小BRISQUE,则可以找出盘的最佳直径Thr*;3) Morphological image processing operations so that if a minimum BRISQUE is found, the optimal diameter Thr* of the disc can be found;
4)使用Otsu的分割二值图像。4) Use Otsu's to segment the binary image.
所提出的分割方法的流程图如附图1所示。A flowchart of the proposed segmentation method is shown in Figure 1.
实验分析和结果,Experimental analysis and results,
为了评估和测试所提出的方法,本方案使用来自加利福尼亚大学伯克利分校东亚图书馆的中文碑帖图像的数据库(http://ucblibrary4.berkeley.edu:8088/xtf3/ search?rmode=stonerubbings&identifier=&title=&name =&text=&date=&startdate=15&subje ct=&height=&width=&material=&script=&enc_provenance=)。本方案用 Jaccard相似系数,FPR(假阳性率)和FNR(假阴性率)与经典的Otsu算法相比,来评估本方案的系统在性能和质量上的表现。如图2-4所示,常规中国碑帖图像的拓片图像,拓片图像偏暗,直方图显示图像对比度很低,历史文档图像质量下降。在估计前景之前,有必要应用中值滤波器去除噪音。中值滤波器依次考虑图像中的每个像素并查看其领域,以确定它是否代表其领域。该邻域被选为3×3像素的正方形。与图像大小相比,这是一个非常小的邻域,最终的图像大小为371×1260像素。之后,在Matlab中使用函数imopen,在估计前景上执行形态学开运算。To evaluate and test the proposed method, this scheme uses a database of Chinese inscription images from the University of California, Berkeley East Asian Library ( http://ucblibrary4.berkeley.edu:8088/xtf3/search?rmode=stonerubbings&identifier=&title=&name =&text=&date=&startdate=15&subject=&height=&width=&material=&script=&enc_provenance=). This program uses the Jaccard similarity coefficient, FPR (false positive rate) and FNR (false negative rate) to compare with the classic Otsu algorithm to evaluate the performance and quality of the system of this program. As shown in Figure 2-4, the rubbing image of the conventional Chinese inscription image is dark, the histogram shows that the image contrast is very low, and the image quality of historical documents is degraded. Before estimating the foreground, it is necessary to apply a median filter to remove noise. A median filter considers each pixel in the image in turn and looks at its sphere to determine if it is representative of its sphere. The neighborhood is chosen as a 3×3 pixel square. This is a very small neighborhood compared to the image size, and the final image size is 371×1260 pixels. Afterwards, a morphological opening is performed on the estimated foreground using the function imopen in Matlab.
将上述介绍的二值化方法应用于我们的测试集合,该测试集合由具有几种降级和结构复杂性的旧中文碑帖文档图像组成。我们在本实施例中介绍将以前方法应用于我们收藏的图像的结果。对于客观评价,我们使用Jaccard系数,假阳性率(FPR)和假阴性率(FNR)三个参数进行分层测量,FPR显示欠分割程度,FNR显示过度分割程度。Jaccard系数测量有限样本集之间的相似性,并且定义为相交的大小除以样本集的并集的大小,对于二值图像,它计算二值图像A和B的交除以A和B的并。Jaccard系数可以使用以下公式计算:The binarization method introduced above is applied to our test set, which consists of images of old Chinese rubbing post documents with several degradations and structural complexities. We present in this example the results of applying previous methods to our collection of images. For objective evaluation, we use the Jaccard coefficient, false positive rate (FPR) and false negative rate (FNR) for stratified measurement. FPR shows the degree of under-segmentation, and FNR shows the degree of over-segmentation. The Jaccard coefficient measures the similarity between finite sample sets and is defined as the size of the intersection divided by the size of the union of the sample sets. For binary images, it calculates the intersection of binary images A and B divided by the union of A and B . The Jaccard coefficient can be calculated using the following formula:
假阳性率FPR和假阴性率FNR定义如下:The false positive rate FPR and false negative rate FNR are defined as follows:
其中FP是误报的数量,真实图像中的白色和二值化图像中的黑色,FN是假阴性的数量,TN是真阴性的数量,N=FP+TN是阴性的总数量。where FP is the number of false positives, white in real images and black in binarized images, FN is the number of false negatives, TN is the number of true negatives, and N=FP+TN is the total number of negatives.
分割算法的性能如表1中所示。The performance of the segmentation algorithm is shown in Table 1.
表1分割的定量测量结果Table 1 Quantitative measurements of segmentation
图5中(d)是使用本方案自适应分割算法获得的汉字分割结果,图5中(c)是通过获得的Ostu'分割算法得到的结果,图5中(d)是基本真实二值图像,指的是手动消除所有噪声和劣化因子的实际二值图像。在表1中,可以注意到低对比度文档图像的最佳结果是通过本方案方法获得的。在表1中,Jaccard系数显着高于Ostu方法。OTSU的全局阈值方法错分了一些文本像素,同时错误地将黑暗背景像素归类为文本像素。实验结果表明,本方案所提出的背景消除算法比Ostu方法对于各种中文碑帖图像可以实现更精确的修复。对于低对比度的中国碑帖图像,它表现良好。(d) in Figure 5 is the Chinese character segmentation result obtained by using the adaptive segmentation algorithm of this scheme, (c) in Figure 5 is the result obtained by the Ostu' segmentation algorithm obtained, and (d) in Figure 5 is the basic real binary image , refers to the actual binary image with all noise and degradation factors manually removed. In Table 1, it can be noticed that the best results for low-contrast document images are obtained by the proposed method. In Table 1, the Jaccard coefficient is significantly higher than the Ostu method. OTSU's global thresholding method misclassifies some text pixels, while misclassifying dark background pixels as text pixels. The experimental results show that the background removal algorithm proposed in this scheme can achieve more accurate restoration of various Chinese rubbing images than the Ostu method. For low-contrast images of Chinese inscriptions, it performs well.
但是,由于阈值是全局应用的,对于某些弱笔迹的阈值,导致笔迹有可能被破坏。However, since the threshold is applied globally, the threshold for some weak handwriting may cause the handwriting to be broken.
综上,拓片获得的中国碑帖图像具有模糊细节多,效果差等特点,因此在传统的处理过程中可能会丢失更多细节。预处理是图像处理中的一个重要阶段,特别是在中国古籍图像分割应用的情况下。一种高效的图像预处理算法将提高分割算法的准确性并减少错误分类。本发明提出了一种针对退化的碑帖图像二值化的自适应分割算法。主观和客观的评价方法被用来判断我们算法的效率。实验结果表明,通过形态学操作估计图像背景是自适应选择以找到磁盘的最佳直径。但是,由于我们的背景估计算法没有考虑不同场景中的照度关系,所以在场景明显变化的情况下,有可能引入其他应用的轻微闪烁。将来,该方法将在OCR (光学字符识别)应用程序中进行测试,以测试所提方法在降级文档中的可读性。In summary, the images of Chinese inscriptions obtained from rubbings have the characteristics of many blurred details and poor effects, so more details may be lost in the traditional processing process. Preprocessing is an important stage in image processing, especially in the case of Chinese ancient book image segmentation applications. An efficient image preprocessing algorithm will improve the accuracy of segmentation algorithms and reduce misclassification. The invention proposes an adaptive segmentation algorithm aiming at the binarization of degraded rubbing image. Subjective and objective evaluation methods are used to judge the efficiency of our algorithm. Experimental results show that estimating the image background by morphological operations is an adaptive choice to find the optimal diameter of the disk. However, since our background estimation algorithm does not consider the illuminance relationship in different scenes, it is possible to introduce slight flicker for other applications when the scene changes significantly. In the future, the method will be tested in an OCR (Optical Character Recognition) application to test the readability of the proposed method in degraded documents.
另外,本发明获得中国国家自然科学基金(61472173),江西省自然科学基金(20161BAB202042),江西省教委资助项目(GJJ151134)的大力支持。In addition, this invention has been strongly supported by the National Natural Science Foundation of China (61472173), the Natural Science Foundation of Jiangxi Province (20161BAB202042), and the Jiangxi Provincial Education Commission Funding Project (GJJ151134).
以上所述仅为本发明的实施例,并非因此限制本发明的专利范围,凡是利用本发明说明书内容所作的等效结构或等效流程变换,或直接或间接运用在其它相关的技术领域,均同理包括在本发明的专利保护范围内。The above descriptions are only examples of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the description of the present invention, or directly or indirectly used in other related technical fields, shall be The same reasoning is included in the patent protection scope of the present invention.
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