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

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

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join). In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP) is used according to the first vision; whereas Local Binary Patterns (LBP) are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB) and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%

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

Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's  identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666)

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

Text-independent chinese writer identification using hybrid SLT-LBP feature

This study proposes a new hybrid method using texture features of input handwriting document image as global to overcome the limitation of data heterogeneity, which causing the ambiguity and leads to inconsistent results apart from problems of scale involve database size. The method first adopts Slantlet Transform (SLT) to bring out hidden texture details prior to feature extractions. Then, Local Binary Pattern (LBP) descriptor is applied on the SLT image to extract texture features. A new hybrid method Slantlet Transform based Local Binary Pattern (SLT-LBP), are experimented on an open and widely used HIT-MW Chinese database for performance evaluation. This study strengthens the idea that to unravel some of data heterogeneity and lead to improve identification performance, especially searching for relevant document from large complex repositories is an essential issue

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

Robust off-line text independent writer identification using bagged discrete cosine transform features

Efficient writer identification systems identify the authorship of an unknown sample of text with high confidence. This has made automatic writer identification a very important topic of research for forensic document analysis. In this paper, we propose a robust system for offline text independent writer identification using bagged discrete cosine transform (BDCT) descriptors. Universal codebooks are first used to generate multiple predictor models. A final decision is then obtained by using the majority voting rule from these predictor models. The BDCT approach allows for DCT features to be effectively exploited for robust hand writer identification. The proposed system has first been assessed on the original version of hand written documents of various datasets and results have shown comparable performance with state-of-the-art systems. Next, blurry and noisy documents of two different datasets have been considered through intensive experiments where the system has been shown to perform significantly better than its competitors. To the best of our knowledge this is the first work that addresses the robustness aspect in automatic hand writer identification. This is particularly suitable in digital forensics as the documents acquired by the analyst may not be in ideal conditions