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.     

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.     

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...     

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...     

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...     

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。     

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.