Off-line Arabic Character-Based Writer Identification – a Survey

Off-line writer identification requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text, which it can be gathered in  on-line writer identification. Various techniques have been implemented to achieve high identification rates. These techniques have tackled different aspects of the identification system. Importance of writer identification system is to help mainly in forensic fields, historical document analysis and  handwriting recognition system enhancement. Unfortunately, the Arabic writer identification system not achieves a satisfaction rate yet whereas certain process of features and classification still not recognized

Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors

Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines Scale Invariant Feature Transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMM). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While a SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates a SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic and one hybrid language) and the results have shown the superiority of the proposed system over state-of-the-art techniques

TEXT CONTENT DEPENDENT WRITER IDENTIFICATION

Text content based personal Identification system is vital in resolving problem of identifying unknown document’s writer using a set of handwritten samples from alleged known writers. Text written on paper document is usually captured as image by scanner or camera for computer processing. The most challenging problem encounter in text image processing is extraction of robust feature vector from a set of inconstant handwritten text images obtained from the same writer at different time. In this work new feature extraction method is engaged to produce active text features for developing an effective personal identification system. The feature formed feature vector which is fed as input data into classification algorithm based on Support Vector Machine (SVM). Experiment was conducted to identify writers of query handwritten texts. Result show satisfactory performance of the proposed system, it was able to identify writers of query handwritten texts

Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

Writer identification using curvature-free features

Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results

Features selection for offline handwritten signature verification: State of the art

This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification

Writer Identification for chinese handwriting

Abstract Chinese handwriting identification has become a hot research in pattern recognition and image processing. In this paper, we present overview of relevant papers from the previous related studies until to the recent publications regarding to the Chinese Handwriting Identification. The strength, weaknesses, accurateness and comparison of well known approaches are reviewed, summarized and documented. This paper provides broad spectrum of pattern recognition technology in assisting writer identification tasks, which are at the forefront of forensic and biometrics based on identification application

Biometrics Writer Recognition for Arabic language: Analysis and Classification techniques using Subwords Features

Handwritten text in any language is believed to convey a great deal of information about writers’ personality and identity. Indeed, handwritten signature has long been accepted as an authentication of the writer’s physical stamp on financial and legal deals as well official/personal documents and works of art. Handwritten documents are frequently used as evidences in forensic tasks. Handwriting skills is learnt and developed from the early schooling stages. Research interest in behavioral biometrics was the main driving force behind the growth in research into Writer Identification (WI) from handwritten text, but recent rise in terrorism associated with extreme religious ideologies spreading primarily, but not exclusively, from the middle-east has led to a surge of interest in WI from handwritten text in Arabic and similar languages. This thesis is the main outcome of extensive research investigations conducted with the aim of developing an automatic identification of a person from handwritten Arabic text samples. My motivations and interests, as an Iraqi researcher, emanate from my multi-faceted desires to provide scientific support for my people in their fight against terrorism by providing forensic evidences, and as contribute to the ongoing digitization of the Iraqi National archive as well as the wealth of religious and historical archives in Iraq and the middle-east. Good knowledge of the underlying language is invaluable in this project. Despite the rising interest in this recognition modality worldwide, Arabic writer identification has not been addressed as extensively as Latin writer identification. However, in recent years some new Arabic writer identification approaches have been proposed some of which are reviewed in this thesis. Arabic is a cursive language when handwritten. This means that each and every writer in this language develops some unique features that could demonstrate writer’s habits and style. These habits and styles are considered as unique WI features and determining factors. Existing dominating approaches to WI are based on recognizing handwriting habits/styles are embedded in certain parts/components of the written texts. Although the appearance of these components within long text contain rich information and clues to writer identity, the most common approaches to WI in Arabic in the literature are based on features extracted from paragraph(s), line(s), word(s), character(s), and/or a part of a character. Generally, Arabic words are made up of one or more subwords at the end of each; there is a connected stroke with a certain style of which seem to be most representative of writers habits. Another feature of Arabic writing is to do with diacritics that are added to written words/subwords, to add meaning and pronunciation. Subwords are more frequent in written Arabic text and appear as part of several different words or as full individual words. Thus, we propose a new innovative approach based on a seemingly plausible hypothesis that subwords based WI yields significant increase in accuracy over existing approaches. The thesis most significant contributions can be summarized as follows: - Developed a high performing segmentation of scanned text images, that combines threshold based binarisation, morphological operation and active shape model. - Defined digital measures and formed a 15-dimensional feature vectors representations of subwords that implicitly cover its diacritics and strokes. A pilot study that incrementally added features according to writer discriminating power. This reduced subwords feature vector dimension to 8, two of which were modelled as time series. - For the dependent 8-dimensional WI scheme, we identify the best performing set of subwords (best 22 subwords out of 49 then followed by best 11 out of these 22 subwords). - We established the validity of our hypothesis for different versions of subwords based WI schemes by providing empirical evidence when testing on a number of existing text dependent and in text-dependent databases plus a simulated text-in text-dependent DB. The text-dependent scenario results exhibited possible present of the Doddington Zoo phenomena. - The final optimal subword based WI scheme, not only removes the need to include diacritics as part of the subword but also demonstrating that including diacritics within subwords impairs the WI discriminating power of subwords. This should not be taken to discredit research that are based on diacritics based WI. Also in this subword body (without diacritics) base WI scheme, resulted in eliminating the presence of Doddington Zoo effect. - Finally, a significant but un-intended consequence of using subwords for WI is that there is no difference between a text-independent scenario and text-dependent one. In fact, we shall demonstrate that the text-dependent database of the 27-words can be used to simulate the testing of the scheme for an in text-dependent database without the need to record such a DB. Finally, we discussed ways of optimising the performance of our last scheme by considering possible ways of complementing our scheme using the addition of various image texture analysis features to be extracted from subwords, lines, paragraphs or entire file of the scabbed image. These included LBP and Gabor Filter. We also suggested the possible addition of few more features