Youcef Chibani

The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters

The limited number of writers and genuine signatures constitutes the main problem for designing a robust Handwritten Signature Verification System (HSVS). We propose, in this paper, the use of One-Class Support Vector Machine (OC-SVM) based on writer-independent parameters, which takes into consideration only genuine signatures and when forgery signatures are lack as counterexamples for designing the HSVS. The OC-SVM is effective when large samples are available for providing an accurate classification. However, available handwritten signature samples are often reduced and therefore the OC-SVM generates an inaccurate training and the classification is not well performed. In order to reduce the misclassification, we propose a modification of decision function used in the OC-SVM by adjusting carefully the optimal threshold through combining different distances used into the OC-SVM kernel. Experimental results conducted on CEDAR and GPDS handwritten signature datasets show the effective use of the proposed system comparatively to the state of the art. We propose handwritten signature verification for writer independent parameters.We propose to design HSVS system using only genuine signatures using OC-SVM.Applying a soft threshold in order to reduce the misclassification of the OC-SVM.Combination scheme is proposed through versus distances used into the OC-SVM kernel.Competitive results are obtained comparatively to the state of the art.

New off-line Handwritten Signature Verification method based on Artificial Immune Recognition System

A new system for Handwritten Signature Verification is proposed.The Artificial Immune Recognition System is proposed to achieve the verification task.Two new features that are Gradient Local Binary Patterns and Longest Run Features are proposed.Results obtained on CEDAR and GPDS-100 corpuses reveal the effectiveness of the proposed methods. Natural Immune System offers many interesting features that inspired the design of Artificial Immune Systems (AIS) used to solve various problems of engineering and artificial intelligence. AIS are particularly successful in fault detection and diagnosis applications where anomalies such as errors and failures are assimilated to viruses that should be detected. Thereby, AIS seem suitable to automatically detect forgeries in signature verification systems. This paper proposes a novel method for off-line signature verification that is based on the Artificial Immune Recognition System (AIRS). For feature generation, two different descriptors are proposed to generate signature traits. The first is the Gradient Local Binary Patterns that estimates gradient features based on the LBP neighborhood. The second descriptor is the Longest Run Feature, which describes the signature topology by considering longest suites of text pixels. Performance evaluation is carried out on CEDAR and GPDS-100 datasets. The results obtained showed that the proposed system has promising performance and often comfortably outperforms the state of the art.

Local descriptors to improve off-line handwriting-based gender prediction

Gender prediction based on the handwritten text becomes to earn a considerable importance for the document analysis community Gender prediction based on the handwritten text becomes to earn a considerable importance for the document analysis community. It is helpful for person identification as well as in some situations where one needs to classify population according to women-men categories. However, only a few studies have been carried out in this field. In the present work, we propose the use of local descriptors in order to improve the gender classification based on offline handwritten text. Specifically, we employ Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) as well as grid features, which are successful in various pattern recognition applications. The prediction task is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset show that local descriptors provide quite promising results.

Ancient degraded document image binarization based on texture features

In this paper, we present a promising method for binarization of historical and degraded document images, based on texture features. The proposed method is an adaptive threshold-based. This latter is computed by using a descriptor based on a co-occurrence matrix. The proposed method is tested objectively, using DIBCO dataset degraded documents and subjectively, using a set of ancient degraded documents provided by a national library. The results are satisfactory and promising, and present an improvement to classical methods.

Robust soft-biometrics prediction from off-line handwriting analysis

Graphical abstractDisplay Omitted Currently, writers soft-biometrics prediction is gaining an important role in various domains related to forensics and anonymous writing identification. The purpose of this work is to develop a robust prediction of the writers gender, age range and handedness. First, three prediction systems using SVM classifier and different features, that are pixel density, pixel distribution and gradient local binary patterns, are proposed. Since each system performs differently to the others, a combination method that aggregates a robust prediction from individual systems, is proposed. This combination uses Fuzzy MIN and MAX rules to combine membership degrees derived from predictor outputs according to their performances, which are modeled by Fuzzy measures. Experiments are conducted on two Arabic and English public handwriting datasets. The comparison of individual predictors with the state of the art highlights the relevance of proposed features. Besides, the proposed Fuzzy MIN-MAX combination comfortably outperforms individual systems and classical combination rules. Relatively to Sugenos Fuzzy Integral, it has similar computational complexity while performing better in most cases.

Age, gender and handedness prediction from handwriting using gradient features

This work introduces two gradient features for writers gender, handedness, and age range prediction. The first feature is the Histogram of Oriented Gradients, which highlights the distribution of gradient orientations within images. The second feature is the so-called gradient local binary patterns, which is an improved gradient feature that incorporates the local binary pattern neighborhood in the gradient calculation. The prediction task is achieved by using SVM classifier. Experiments are performed on two corpuses of English and Arabic handwritten text. The results obtained in terms of classification accuracy highlight the effectiveness of the proposed features, which overcome the state of the art.

Off-line signature verification using artificial immune recognition system

In various pattern recognition applications, artificial immune systems achieve comparable and commonly higher performance than other classification schemes such as SVM. In this paper, we investigate their applicability for handwritten signature verification. Specifically, Ridgelet transform and grid features are used to extract pertinent characteristics. Performance assessment is conducted on the CEDAR dataset comparatively to SVM classifiers. The results in terms of average error rate highlight the high performance of artificial immune recognition algorithm.

Machine printed handwritten text discrimination using Radon transform and SVM classifier

Discrimination of machine printed and handwritten text is deemed as major problem in the recognition of the mixed texts. In this paper, we address the problem of identifying each type by using the Radon transform and Support Vector Machines, which is conducted at three steps: preprocessing, feature generation and classification. New set of features is generated from each word using the Radon transform. Classification is used to distinguish printed text from handwritten. The proposed system is tested on IAM databases. The recognition rate of the proposed method is calculated to be over 98%.

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.

Handwritten Arabic word recognition based on Ridgelet transform and support vector machines

We propose a method for handwritten Arabic word recognition based on the combination of the Ridgelet transform and SVMs. Ridgelets are used for generating pertinent features of handwritten words while the classification stage is based on the One-Against-All multiclass implementation of SVMs. The experimental investigation is conducted on a vocabulary of twenty-four words extracted from the IFN/ENIT database. The Ridgelet performance is assessed comparatively to the results obtained for Radon and uniform grid (zoning) features. Results highlight the reliability of the Ridgelet-SVM combination for handwritten Arabic word recognition.