基于局部结构分割的维吾尔文笔迹鉴别方法

针对基于纹理分析的全局特征提取受文本组合或字符连接影响较大,导致特征提取不稳定的现象,以及现有局部特征提取方法存在的不足,提出一种基于局部结构分割构建Codebook的维吾尔文文本无关笔迹鉴别方法。该方法根据笔迹图像的像素值提取维吾尔文笔迹样本中具有代表性的轮廓,生成描述书写者书写风格的Codebook并进行相似性度量,从而达到笔迹鉴别的目的。实验结果表明,该方法对于维吾尔文笔迹是一种简单、可行,具有较高识别率的笔迹鉴别方法。     

Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents

We present our work on the paleographic analysis and recognition system intended for processing of historical Hebrew calligraphy documents. The main goal is to analyze documents of different writing styles in order to identify the locations, dates, and writers of test documents. Using interactive software tools, a data base of extracted characters has been established. It now contains about 20,000 characters of 34 different writers, and will be distinctly expanded in the near future. Preliminary results of automatic extraction of pre-specified letters using the erosion operator are presented. We further propose and test topological features for handwriting style classification based on a selected subset of the Hebrew alphabet. A writer identification experiment using 34 writers yielded 100% correct classification.     

Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.     

WRITER IDENTIFICATION OF ARABIC TEXT USING STATISTICAL AND STRUCTURAL FEATURES

This article addresses writer identification of handwritten Arabic text. Several types of structural and statistical features were extracted from Arabic handwriting text. A novel approach was used to extract structural features that build on some of the main characteristics of the Arabic language. Connected component features for Arabic handwritten text as well as gradient distribution features, windowed gradient distribution features, contour chain code distribution features, and windowed contour chain code distribution features were extracted. A nearest neighbor (NN) classifier was used with the Euclidean distance measure. Data reduction algorithms (viz. principal component analysis [PCA], linear discriminant analysis [LDA], multiple discriminant analysis [MDA], multidimensional scaling [MDS], and forward/backward feature selection algorithm) were used. A database of 500 paragraphs handwritten in Arabic by 250 writers was used. The paragraphs used were randomly generated from a large corpus. NN provided the best accuracy in text-independent writer identification with top-1 result of 88.0%, top-5 result of 96.0%, and top-10 result of 98.5% for the first 100 writers. Extending the work to include all 250 writers and with the backward feature selection algorithm (using 54 out of 83 features), the system attained a top-1 result of 75.0%, top-5 result of 91.8%, and top-10 result of 95.4%.     

一种鲁棒的离线笔迹鉴别方法

离线笔迹鉴别在司法鉴定与历史文档分析中有重要作用.当前的主要离线笔迹鉴别都是基于局部特征提取的方法,其在笔迹检索中严重依赖于数据增强和全局编码,在笔迹识别中需要较多的笔迹信息.针对这一问题,本文提出一种基于统计的文档行分割与深度卷积神经网络相结合的离线笔迹鉴别方法(DLS-CNN).首先,使用基于统计的文档行分割方法将笔迹材料分割成小的像素块;然后,用优化后的残差神经网络作为识别模型;最后,对局部特征使用取均值法进行编码.在ICDAR2013和CVL这两个标准数据集上的实验结果表明,该方法能有效获得鲁棒的局部特征,从而仅需要少量的笔迹信息就能取得较高的识别率,而且不需依赖于数据增强和全局编码就能取得较好的检索效果.实验代码地址:https://github.com/shiming-chen/DLS-CNN.     

基于混合码本与因子分析的文本独立笔迹鉴别

针对已有的笔迹鉴别方法对笔迹版式的要求比较严格、训练过程耗时、对内容不受限制的小样本数据情况下鉴别性能较低等问题, 提出了基于混合码本与因子分析的文本独立笔迹鉴别算法. 该算法提取写作时常用的子图像, 并用描述符标注“代码”建立“码本”. 在特征提取层, 分别采用加权的方向指数直方图法和距离变换法, 对于具有相同描述符的“代码”计算特征距离. 把影响特征距离的因素分为书写因子和字符因子, 对码本中的每个书写模式进行双因子方差分析. 在IAM和Firemaker这两个标准数据集上的实验结果证明, 相比目前国内外的先进已有方法, 本文提出的算法在精度和速度方面有一定的优势, 具有一定的推广价值, 适合处理多语种的笔迹鉴别问题.     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.     

HMM-based writer identification in music score documents without staff-line removal

Writer identification from musical score documents is a challenging task due to its inherent problem of overlapping of musical symbols with staff-lines. Most of the existing works in the literature of writer identification in musical score documents were performed after a pre-processing stage of staff-lines removal. In this paper we propose a novel writer identification framework in musical score documents without removing staff-lines from the documents. In our approach, Hidden Markov Model (HMM) has been used to model the writing style of the writers without removing staff-lines. The sliding window features are extracted from musical score-lines and they are used to build writer specific HMM models. Given a query musical sheet, writer specific confidence for each musical line is returned by each writer specific model using a log-likelihood score. Next, a log-likelihood score in page level is computed by weighted combination of these scores from the corresponding line images of the page. A novel Factor Analysis-based feature selection technique is applied in sliding window features to reduce the noise appearing from staff-lines which proves efficiency in writer identification performance. In our framework we have also proposed a novel score-line detection approach in musical sheet using HMM. The experiment has been performed in CVC-MUSCIMA data set and the results obtained show that the proposed approach is efficient for score-line detection and writer identification without removing staff-lines. To get the idea of computation time of our method, detail analysis of execution time is also provided.     

Individuality of alphabet knowledge in online writer identification

Allograph prototype approaches for writer identification have been gaining popularity recently due to its simplicity and promising identification rates. Character prototypes that are used as allographs produce a consistent set of templates that models the handwriting styles of writers, thereby allowing high accuracies to be attained. We hypothesize that the alphabet knowledge inherent in such character prototypes can provide additional writer information pertaining to their styles of writing and their identities. This paper utilizes a character prototype approach to establish evidence that knowledge of the alphabet offers additional clues which help in the writer identification process. This paper then introduces an alphabet information coefficient (AIC) to better exploit such alphabet knowledge for writer identification. Our experiments showed an increase in writer identification accuracy from 66.0 to 87.0% on a database of 200 reference writers when alphabet knowledge was used. Experiments related to the reduction in dimensionality of the writer identification system are also reported. Our results show that the discriminative power of the alphabet can be used to reduce the complexity while maintaining the same level of performance for the writer identification system.     

局部轮廓结构编码的笔迹鉴别

提出一种基于滑动窗口的局部轮廓结构特征的文本无关的笔迹鉴别方法。该方法利用概率分布函数对笔迹中出现的各类局部轮廓形状结构的分布进行了描述,并采用卡方距离度量方法对笔迹进行最终的相似性度量。实验结果表明,在包含240人的HIT-MW中文笔迹库上有效地提高了鉴别正确率。     

基于特征融合的脱机中文笔迹鉴别

提出一种基于文本依存笔迹特征融合的文本独立特征构造方法。建立基于方向指数直方图法笔迹特征(文本依存特征)的两因子分解模型。笔迹特征可分解成字符因子和书写因子两部分。通过两因子方差分析与数据挖掘,分离出与字符无关的书写因子,得到基于文本依存方法的文本独立特征。该方法对检材与样本笔迹的字符数量较少,特别是相同字很少或是根本没有相同字的情况下,能取得较理想的笔迹鉴别准确率,为少量字笔迹鉴别提供解决问题的思路。     

基于手写体的身份识别

以前许多文章曾介绍过一些基于手写体的书写人身份识别技术，其中多数都假设所写的文本是固定的。本文中，我们试图通过一种自动的不依赖文本的书写人识别听新颖算法，来消除这种假设，假定不同的人手写体存在明显的区别，我们采用一种综合方法，它基于纹理分析，每个人的手写体都被看成一种不同的纹理。原则上，我们可以采用任意一种标准的纹理识别算法（例如：多通道伽柏滤波器方法）。在对40名书写人的1000份测试文档的分类中，测试结果非常令人满意，识别率最高达到了96%。     

Interval valued symbolic representation of writer dependent features for online signature verification

This work focusses on exploitation of the notion of writer dependent parameters for online signature verification. Writer dependent parameters namely features, decision threshold and feature dimension have been well exploited for effective verification. For each writer, a subset of the original set of features are selected using different filter based feature selection criteria. This is in contrast to writer independent approaches which work on a common set of features for all writers. Once features for each writer are selected, they are represented in the form of an interval valued symbolic feature vector. Number of features and the decision threshold to be used for each writer during verification are decided based on the equal error rate (EER) estimated with only the signatures considered for training the system. To demonstrate the effectiveness of the proposed approach, extensive experiments are conducted on both MCYT (DB1) and MCYT (DB2) benchmarking online signature datasets consisting of signatures of 100 and 330 individuals respectively using the available 100 global parametric features.     

一种基于微结构特征的多文种文本无关笔迹鉴别方法

与字符识别一样, 计算机自动笔迹鉴别是一个涉及到不同文种的研究课题. 本文提出了一种基于网格窗口微结构特征的文本无关的笔迹鉴别方法, 能适用于各种不同文种的笔迹. 该方法对笔迹中局部细微结构的书写变化趋势进行描述, 并采用加权距离度量方法进行笔迹相似性度量. 利用该方法实现了文本无关的多文种笔迹检索系统, 并在实际汉字、英文、藏文和维吾尔文的笔迹库上进行了测试. 实验证明, 该方法是一种高效且适用性较广、限制性较少的笔迹鉴别方法.     

Automatic writer identification from text line images

In the present article, new techniques have been introduced for revealing the individual features of a person??s handwriting pattern from the scanned images of handwritten text lines to facilitate text-independent writer identification. These techniques are aimed at designing a dynamic model which can be formalized according to any handwritten text line. Various combinations of the extracted features are applied to three well known classifiers for evaluating the contribution of features to define the correct identification rate. The K-NN, GMM, and Normal Density Discriminant Function Bayes classifiers are used in the present identification model. The experimental studies are conducted using two datasets obtained from the IAM database. The first dataset has already been proposed and used in the literature, whereas the second dataset is an expanded version of the first dataset and has been constituted for the first time in this study to analyze the performance of the extracted features under conditions such as an increased number of writers to discriminate in the database and a decreased number of text lines per writer. The remarkable identification rates obtained from the three classifiers on both datasets clearly indicate that the proposed feature extraction techniques can be effectively used in writer identification systems.     

Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features

We propose an effective method for automatic writer recognition from unconstrained handwritten text images. Our method relies on two different aspects of writing: the presence of redundant patterns in the writing and its visual attributes. Analyzing small writing fragments, we seek to extract the patterns that an individual employs frequently as he writes. We also exploit two important visual attributes of writing, orientation and curvature, by computing a set of features from writing samples at different levels of observation. Finally we combine the two facets of handwriting to characterize the writer of a handwritten sample. The proposed methodology evaluated on two different data sets exhibits promising results on writer identification and verification.     

基于多通道分解与匹配的笔迹鉴别研究

笔迹鉴别是通过分析手写字符的书写风格来判断书写人身份的一门技术.笔迹鉴别 的关键步骤是提取反映书写风格的笔迹特征.笔迹特征包括笔划位置、方向、搭配关系等,它 们可以通过图像多通道分解提取和表达出来.本文提出一种用于笔迹鉴别的二值图像多通道 分解方法,利用字符的笔划方向性先进行方向分解,然后对每个方向的子图像进行频带分解. 用分解后的采样信号值作为笔迹特征,用特征匹配方法进行书写人识别,得到了很好的实验 结果.     

基于SVM和D-S理论的三维人脸识别

针对三维人脸识别算法中的高精度分类器设计问题,采用人脸全局特征和局部特征共四个相互独立的多特征信息分类后进行D-S数据融合技术来实现。通过SVM分类器对三维人脸图像中相互独立的全局特征（面廓）和局部特征（眼睛、鼻子和嘴）共四个特征进行一对一的单特征识别,并将其结果进行数据归一化处理后,作为D-S证据理论的BPA,按照D-S理论融合全局特征和局部特征数据,计算出更加准确的识别结果。经过融合数据结果分析,发现该算法可靠有效,大大提高了三维人脸的识别效率。