An effective DeepWINet CNN model for off-line text-independent writer identification

Pattern Analysis and Applications - Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has...     

离线手写体笔迹鉴别方法研究

笔迹鉴别可分为在线、离线两种。主要针对现有的离线手写体笔迹鉴别方法展开研究,重点集中在笔迹图像预处理、特征提取、分类、鉴别过程和效果评价等方面,探讨了各种方法的优点和不足,并提出了今后一些可能的研究方向和内容。     

Extraction and analysis of forensic document examiner features used for writer identification

In this paper we present a study of structural features of handwriting extracted from three characters “d”, “y”, and “f” and grapheme “th”. The features used are based on the standard features used by forensic document examiners. The process of feature extraction is presented along with the results. Analysis of the usefulness of features was conducted via searching the optimal feature sets using the wrapper method. A neural network was used as a classifier and a genetic algorithm was used to search for optimal feature sets. It is shown that most of the structural micro features studied, do possess discriminative power, which justifies their use in forensic analysis of handwriting. The results also show that the grapheme possessed significantly higher discriminating power than any of the three single characters studied, which supports the opinion that a character form is affected by its adjacent characters.     

A combination of features for symbol-independent writer identification in old music scores

The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers.     

基于多分辨几何特征的维吾尔文脱机签名识别

对维吾尔文手写签名图像进行二值化、去噪、归一化和细化等预处理的基础上,结合维吾尔文手写签名的结构与书写风格,对每幅签名图像进行金字塔式分辨率子图像切分,对高分辨率层抽取了共16维方向特征,对低分辨率层则抽取了共32维局部中心点特征。基于这两种特征的签名识别率分别为95.50%和90.50%。为了进一步提高识别率,又对两种特征进行了融合,结果识别率提升到了98.50%。对比分析了基于欧式距离和卡方距离度量方法对识别率的影响,确定最佳度量方法。     

Text-independent writer identification and verification using textural and allographic features

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates     

Automatic writer identification using connected-component contours and edge-based features of uppercase Western script

In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO3s) in uppercase handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the uppercase character set. Using a codebook of CO3s from an independent training set of 100 writers, the probability-density function (PDF) of CO3s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO3 PDF for identifying individual writers on the basis of a single sentence of uppercase characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO3 PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.     

A writer identification system for on-line whiteboard data

In this paper we address the task of writer identification of on-line handwriting captured from a whiteboard. Different sets of features are extracted from the recorded data and used to train a text and language independent on-line writer identification system. The system is based on Gaussian mixture models (GMMs) which provide a powerful yet simple means of representing the distribution of the features extracted from the handwritten text. The training data of all writers are used to train a universal background model (UBM) from which a client specific model is obtained by adaptation. Different sets of features are described and evaluated in this work. The system is tested using text from 200 different writers. A writer identification rate of 98.56% on the paragraph and of 88.96% on the text line level is achieved.     

Feature learning and encoding for multi-script writer identification

International Journal on Document Analysis and Recognition (IJDAR) - Writer identification from handwriting samples has been an interesting research problem for the pattern recognition community in...     

Assessment of geometric features for individual identification and verification in biometric hand systems

This paper studies the reliability of geometric features for the identification of users based on hand biometrics. Our methodology is based on genetic algorithms and mutual information. The aim is to provide a system for user identification rather than a classification. Additionally, a robust hand segmentation method to extract the hand silhouette and a set of geometric features in hard and complex environments is described. This paper focuses on studying how important and discriminating the hand geometric features are, and if they are suitable in developing a robust and reliable biometric identification. Several public databases have been used to test our method. As a result, the number of required features have been drastically reduced from datasets with more than 400 features. In fact, good classification rates with about 50 features on average are achieved, with a 100% accuracy using the GA–LDA strategy for the GPDS database and 97% for the CASIA and IITD databases, approximately. For these last contact-less databases, reasonable EER rates are also obtained.     

基于复小波的脱线手写体笔迹鉴别

进行脱线笔迹鉴别时,笔迹特征只能从手写体图像中提取,且无法获取书写时的动态信息,导致了脱线笔迹鉴别的正确率不是很高。为了进一步提高脱线手写体笔迹鉴别的正确率,提出基于复小波的GGD模型方法对笔迹进行鉴别。与传统小波GGD模型方法比较,复小波GGD模型方法具有时移不变性和良好的方向分析能力,在提取纹理特征方面更有效。实验结果表明,该方法在鉴别正确率上有很大的提升。     

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.     

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.     

WriterINet: a multi-path deep CNN for offline text-independent writer identification

International Journal on Document Analysis and Recognition (IJDAR) - Handwriting-based identification is a fundamental pattern recognition problem that has attracted considerable interest in recent...     

Automatic writer identification framework for online handwritten documents using character prototypes

This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.     

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.     

Robust vision-based features and classification schemes for off-line handwritten digit recognition

We use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images.