Off-line Writer Identification Research Articles

Arabic Offline writer identification on a new version of AHTID/MW database

Arabic Offline writer identification on a new version of AHTID/MW database

Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

Offline Writer Identification using Convolutional Recurrent Neural Network

Writer identification can be used to find fakes and help with forensic science. Handwritten text is the only way to identify the author offline. Recently, convolutional neural networks (CNNs) have become the best way to classify images on a large scale. The recurrent neural network (RNN) models the spatial relationship between the fragment sequences in order to improve the local fragment feature's capacity for discrimination. To get the best of both models, combine CNN and RNN to create a Convolution Recurrent Neural Network (CRNN). Therefore, the CRNN model for offline writer identification is suggested in this paper. The proposed method achieved 96% accuracy in classifying 690 writers using CRNN. The suggested method can make efficient and strong writer identification based on different sizes, different orientations, or both. The efficiency of the proposed system has been demonstrated. Finally, an accuracy comparison of CRNN with CNN and RNN has been conducted.

Offline writer identification using a developed deep neural network based on a novel signature dataset

Offline writer identification using a developed deep neural network based on a novel signature dataset

Off-Line Writer Identification Based on Small Segments of Handwritten Text and Convolutional Neural Networks

Off-Line Writer Identification Based on Small Segments of Handwritten Text and Convolutional Neural Networks

Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes

The writer’s identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (“ S ”, “ ∩ ”, “ C ”, “ ∼ ” and “ U ”) has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance.

Encoding Pathlet and SIFT Features With Bagged VLAD for Historical Writer Identification

Offline writer identification plays an important role in forensic document examination and historical document analysis. Today, challenges still exist in historical writer identification (WI), where documents may present very complex handwriting styles. In this paper, we propose novel techniques for a detailed description and accurate identification of handwriting in historical documents. Because handwriting contours are one of the most salient components to characterize one’s handwriting style, a novel pathlet feature is proposed to describe their rich properties beyond slant and curvature in a principled way; these properties can be exploited in a VLAD-like encoding framework for fine-grained handwriting description. Besides the pathlet feature, we extract unidirectional SIFT feature to describe handwriting corners and junctions. To effectively encode the pathlet and SIFT features, a novel encoding method, named bagged VLAD, is further proposed to address the problem that a large codebook sparsely spreads out the data points and leads to a degraded performance, allowing a much larger codebook for improved encoding performance. Our proposed method achieves state-of-the-art performance on ICDAR2017 Historical-WI database and ICDAR2019 HDRC-IR database, and has won the first place in ICDAR2019 HDRC-IR competition.

An effective and conceptually simple feature representation for off-line text-independent writer identification

Feature engineering forms an important component of machine learning and pattern recognition. It is a fundamental process for off-line writer identification of handwritten documents, which continues to be an interesting subject of research in various forensic and authentication areas. In this work, we propose an efficient, yet computationally and conceptually simple framework for off-line text independent writer identification using local textural features in characterizing the writing style of each writer. These include Local Binary Patterns (LBP), Local Ternary Patterns (LTP), and Local Phase Quantization (LPQ). Our approach focuses on exploiting the writing images at small observation regions where a set of connected component sub-images are cropped and extracted from each handwriting sample (document or set of word/text line images). These connected components are seen as texture images where each one of them is subjected to feature extraction using LBP, LPQ, or LTP. Then, a histogram sequence concatenation is applied to the feature image after dimensionality reduction followed by image subdivision into a number of non-overlapping regions. For classification, the 1-NN (Nearest Neighbor) classifier is used to identify the writer of the questioned samples based on the dissimilarity of feature vectors computed from all components in the writing. Experiments on IFN/ENIT (411 writers/Arabic), AHTID/MW (53 writers/Arabic), CVL (309 writers/English), and IAM (657 writers/English) databases demonstrate that our proposed system outperforms old and recent state-of-the-art writer identification systems on Arabic script, and demonstrates a competitive performance on English ones.

Content Independent Writer Identification on Bangla Script: A Document Level Approach

Offline writer identification is one of the major fields of study in behavioral biometric. It is a process of matching a questioned document with other documents of known writers to find the appropriate writer. In this paper, local handwriting-based attributes are used as features, and multi-layer perceptron and simple logistic classifiers are used for decision making. The method is tested on an unconstrained handwritten Bangla database of 1383 documents with variable number of datasets from 190 writers. Experimental results show the effectiveness of our system, since it outperforms the state-of-the-art methods by approximately 3% (top-3 and top-4 choices). Further, our method is approximately 27 times faster than conventional segmentation-based methods.

A Set of Features for Text-Independent Writer Identification

Handwriting is considered to be one of the commonly used biometric modality to identify persons in forensic application. In this paper, we propose a new and efficient approach for off-line text-independent writer identification. Indeed, complete section of connected ink race (Connected-Component or CC), delimited only by where the writer has lifted the pen is inspected in this approach relatively to its aspects of directionality, angularity and length. A set of features is extracted from pre-processed CCs to characterize the writer of a handwritten sample. In order to enhance the performance of our writer identification system, we have combined the features. Experiments are made using handwritings from different people of two datasets, the IFN/ENIT dataset which consists of 1750 documents from 350 writers, and the ICDAR2013 dataset which contains 1000 documents from 250 writers. Our approach showed better properties than most of the surveyed techniques in terms of supported corpus size and identification rate.

A hierarchical Dirichlet process mixture of generalized Dirichlet distributions for feature selection

This paper addresses the problem of identifying meaningful patterns and trends in data via clustering (i.e. automatically dividing a data set into meaningful homogenous sub-groups such that the data within the same sub-group are very similar, and data in different sub-groups are very different). The clustering framework that we propose is based on the generalized Dirichlet distribution, which is widely accepted as a flexible modeling approach, and a hierarchical Dirichlet process mixture prior. A main advantage of the adopted hierarchical Dirichlet process is that it provides a principled elegant nonparametric Bayesian approach to model selection by supposing that the number of mixture components can go to infinity. In addition to capturing the structure of the data, the combination of hierarchical Dirichlet process and generalized Dirichlet distribution is shown to offer a natural efficient solution to the feature selection problem when dealing with high-dimensional data. We develop two variational learning approaches (i.e. batch and incremental) for learning the parameters of the proposed model. The batch algorithm examines the entire data set at once while the incremental one learns the model one step at a time (i.e. update the model’s parameters each time new data are introduced). The utility of the proposed approach is demonstrated on real applications namely face detection, facial expression recognition, human gesture recognition, and off-line writer identification. The obtained results show clearly the merits of our statistical framework.

Off-line writer identification using an ensemble of grapheme codebook features

Off-line writer identification is the process of matching a handwritten sample with its author. Manual identification is very time-consuming because it requires a meticulous comparison of character shape details. Consequently the automation of writer identification has become an important area of research interest. The codebook (or bag of features) approach is a state-of-the-art computerized technique for writer identification. One way to achieve a high identification rate is to expose the personalized set of character shapes, or allographs, that a writer has adopted over the years. The main problem associated with this approach is the extremely large of number of points of interest that are generated. In this paper we extend the basic model to include an ensemble of codebooks. Additionally, Kernel discriminant analysis using spectral regression (SR-KDA) is used as a dimensionality reduction technique in order to avoid over-fitting. Fusion of multiple codebooks is shown to increase the identification rate by 11% compared with a single codebook approach.

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.

State of the art in off-line writer identification of handwritten text and survey of writer identification of Arabic text

In this paper we present the state of the art in writer identification and verification of handwritten text. In addition, a special and extensive survey of writer identification and verification of Arabic handwritten text is also included. Different feature extraction techniques are addressed showing the different research groups’ efforts as well as individual efforts. The different classification approaches, e.g. minimum distance classifiers and statistical classifiers, used for identification by writer and verification by different groups and individuals are presented. Identification results of surveyed publications are investigated and tabulated for ease of reference. Examples of writer identification and verification of others languages are addressed. An extensive survey of databases used in writer identification and verification for Latin and Arabic text is presented. Conclusions relevant to writer identification of Arabic text are discussed and future directions stated. Key words: Writer identification, writer verification, handwritten database, feature extraction, handwriting analysis, distance measures, literature survey.

WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people.

Writer identification using directional ink-trace width measurements

As suggested by modern paleography, the width of ink traces is a powerful source of information for off-line writer identification, particularly if combined with its direction. Such measurements can be computed using simple, fast and accurate methods based on pixel contours, the combination of which forms a powerful feature for writer identification: the Quill feature. It is a probability distribution of the relation between the ink direction and the ink width. It was tested in writer identification experiments on two datasets of challenging medieval handwriting and two datasets of modern handwriting. The feature achieved a nearest-neighbor accuracy in the range of 63-95%, which even approaches the performance of two state-of-the-art features in contemporary-writer identification (Hinge and Fraglets). The feature is intuitive and explainable and its principle is supported by a model of trace production by a quill. It illustrates that ink width patterns are valuable. A slightly more complex variant of Quill, QuillHinge, scored 70-97% writer identification accuracy. The features are already being used by domain experts using a graphical interface.

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.

Handwriting-based writer identification with complex wavelet transform

A challenging problem of off-line text-independent writer identification is that plenty of dynamic writing information with the handwriting images can not be extracted as writing features,this results in high error rate in off-line writer identification.In order to enhance the performance of off-line writer identification,a complex wavelet-based Generalized Gaussian Distribution(GGD) method was proposed.Compared with the traditional wavelet-based GGD method,the novel method is more efficient on texture extraction due to its time-invariant features and good directional analysis.Experimental results show that the proposed method achieves a better performance of writer identification.

Bio-Inspired Generalized Global Shape Approach for Writer Identification

Abstract—Writer identification is one of the areas in pattern recognition that attract many researchers to work in, particularly in forensic and biometric application, where the writing style can be used as biometric features for authenticating an identity. The challenging task in writer identification is the extraction of unique features, in which the individualistic of such handwriting styles can be adopted into bio-inspired generalized global shape for writer identification. In this paper, the feasibility of generalized global shape concept of complimentary binding in Artificial Immune System (AIS) for writer identification is explored. An experiment based on the proposed framework has been conducted to proof the validity and feasibility of the proposed approach for off-line writer identification.