Labiba Souici-Meslati

Text-independent writer recognition using multi-script handwritten texts

This paper presents a text-independent writer recognition method in a multi-script environment. Handwritten texts in Greek and English are considered in this study. The objective is to recognize the writer of a handwritten text in one script from the samples of the same writer in another script and hence validate the hypothesis that writing style of an individual remains constant across different scripts. Another interesting aspect of our study is the use of short handwritten texts which was implied to resemble the real life scenarios where the forensic experts, in general, find only short pieces of texts to identify a given writer. The proposed method is based on a set of run-length features which are compared with the well-known state-of-the-art features. Classification is carried out using K-Nearest Neighbors (K-NN) and Support Vector Machines (SVM). The experimental results obtained on a database of 126 writers with 4 samples per writer show that the proposed scheme achieves interesting performances on writer identification and verification in a multi-script environment.

Automatic analysis of handwriting for gender classification

This paper presents a study to predict gender of individuals from scanned images of their handwritings. The proposed methodology is based on extracting a set of features from writing samples of male and female writers and training classifiers to learn to discriminate between the two. Writing attributes like slant, curvature, texture and legibility are estimated by computing local and global features. Classification is carried out using artificial neural networks and support vector machine. The proposed technique evaluated on two databases under a number of scenarios realized interesting results on predicting gender from handwriting.

Artificial Immune Recognition System for Arabic writer identification

Artificial Immune Systems (AIS) is an emerging bio-inspired computer science technique which embody the principles of biological immune systems for tackling complex real-world problems such as pattern recognition. Among the several immune-computing models, Artificial Immune Recognition System (AIRS) is one of the widely used for classification problems. Meanwhile, the issues related to writer identification are currently at the heart of numerous concerns in our modern days society. Writer identification for Arabic text is receiving a renewed attention. Many popular machine learning techniques have been used in writer identification systems but only one limited attempt has been done with AIS. In this paper, we apply AIRS to perform Arabic writer identification based on a set of features extracted from Grey Level Co-occurrence Matrices. Some feature selection techniques are applied to improve computation time and accuracy results. Three traditional classifiers have also been used in our experiments for performance comparison. The obtained results show the promising ability of AIRS in Writer identification.

Writer recognition on arabic handwritten documents

Recognizing the writer of a handwritten document has been an active research area over the last few years and is at the heart of many applications in biometrics, forensics and historical document analysis. In this paper, we present a novel approach for text-independent writer recognition from Arabic handwritten documents. To characterize the handwriting styles of different writers involved in the evaluation of our approach, we have used two texture methods based on edge hinge features and run-lengths features. The efficiency of the proposed approach is demonstrated experimentally by the classification of 1375 handwritten documents collected from 275 different Arabic writers.

LAMIS-MSHD: A Multi-script Offline Handwriting Database

This paper introduces a new offline handwriting database that was developed to be employed in performance evaluation, result comparison and development of new methods related to handwriting analysis and recognition. The database can particularly be used for signature verification, writer recognition and writer demographics classification. In addition, the database also supports isolated digit recognition, digit/text segmentation and recognition and similar related tasks. The database comprises 600 Arabic and 600 French text samples, 1300 signatures and 21,000 digits. 100 Algerian individuals coming from different age groups and educational backgrounds contributed to the development of database by providing a total of 1300 forms. The database is also accompanied with ground truth data supporting the evaluation of the aforementioned tasks. The main contribution of the database is providing a multi-script platform where same authors contributed samples in French and Arabic. It would be interesting to explore applications like writer recognition and writer demographics classification in a multi-script environment.

Multi-script Writer Identification Optimized with Retrieval Mechanism

Identifying the writer of a handwritten document has been an active research area over the last few years with applications in biometrics, forensics, smart meeting rooms and historical document analysis. In this paper, we present a new writer identification system based on a retrieval mechanism. Texture based edge-hinge and run-length features are used to characterize the writing style of an individual. The effectiveness of the proposed system is evaluated on a total of 1583 writing samples in Arabic, German, English, French, and Greek from two different databases. The experimental evaluations reveal that reducing the search space using a writer retrieval mechanism prior to identification improves the identification rates.

ICDAR2015 competition on Multi-script Writer Identification and Gender Classification using ‘QUWI’ Database

This competition targets writer identification and gender classification from offline handwritten documents using the QUWI database. The most interesting aspect of the competition is the use of a dataset with writing samples of the same individual in Arabic as well as English. The competition not only allows an objective comparison of different systems but also permits to investigate the performance of traditional script-dependent systems in a multi-script experimental setup. This paper describes the competition details including the competition tasks, the database employed, the methods used by the participating systems, evaluation and ranking criteria and the overall rankings of the participants. The competition received a total of 13 submissions from 8 different institutions. Writer identification tasks received 5 while the gender classification tasks received 8 submissions.

Codebook for Writer Characterization: A Vocabulary of Patterns or a Mere Representation Space?

Codebook-based representations have been effectively employed for writer identification. Most of the codebook-based methods generate a codebook by clustering a set of patterns extracted from an independent data set. The probability of occurrence of the codebook patterns in a given writing is then used to characterize its author. This study investigates the hypothesis that the codebook is merely a representation space and the codebook patterns themselves do not affect the writer identification performance. The idea is validated by first using codebooks in different scripts from those of writings in question and then by using a synthetically generated codebook. A number of data sets with handwritten samples in Arabic, French, English, German, Urdu and Greek are considered in our series of evaluations. Experiments conducted with different codebooks report interesting results which validate the ideas put forward in this study.

Signature Verification for Offline Skilled Forgeries Using Textural Features

This study explores the effectiveness of two textural measurements on signature verification for skilled forgeries. These texture features include 2D autoregressive coefficients and run-length distributions. Signature images corresponding to 521 writers from the GPDS960 database were used to evaluate the performance of these features. Comparison of the proposed textural features with a number of state-of-the-art features realized interesting results. The run-length features out perform other features for a sufficient number of genuine signatures in the training dataset.

Classical and swarm-based approaches for feature selection in spam filtering

Feature selection is a significant stage in classification and data mining systems. As a preprocessing step to machine learning, it is very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. Swarm intelligence, dealing with natural and artificial systems, composed of many individuals that coordinate using decentralised control and self-organisation, represents an interesting recent trend for feature selection. In this article, we propose several algorithms for feature selection based on classical feature ranking methods, in addition to different variants of ant colony optimisation and binary particle swarm optimisation for e-mail classification. This work presents a comparative study about the performance of these approaches when they are applied in conjunction with Naive Bayes classifiers. Our experimental results show that the proposed feature selection approaches can achieve significant predictive error rates in spam filtering; furthermore, the number of selected features is significantly decreased. These results were confirmed with our experiments on other public databases.