Abdeljalil Gattal

Evaluation of Texture Features for Offline Arabic Writer Identification

Biometric identification of persons has mainly been based on fingerprints, face, iris and other similar attributes. We propose a handwriting-based biometric identification system using a large database of Arabic handwritten documents. The system first extracts, from each handwritten sample, a set of features including run lengths, edge-hinge and edge-direction features. These features are used by a Multiclass SVM (Support Vector Machine) classifier. Experiments are conducted on a new large database of Arabic handwritings contributed by 1000 writers. The highest identification rate achieved by the combination of run-length and edge-hinge features stands at 84.10%.

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.

SVM-Based Segmentation-Verification of Handwritten Connected Digits Using the Oriented Sliding Window

We propose in this paper a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-verification of handwritten connected digits based conjointly on the oriented sliding window and support vector machine (SVM) classifiers. The proposed approach allows separating adjacent digits according the connection configuration by finding at the same time the interconnection points between adjacent digits and the cutting path. SVM-based segmentation-verification using the global decision module allows the rejection or acceptance of the processed image. Experimental results conducted on a large synthetic database of handwritten digits show the effective use of the oriented sliding window for segmentation-verification.

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.

Improving Isolated Digit Recognition Using a Combination of Multiple Features

This paper investigates the combination of different statistical and structural features for recognition of isolated handwritten digits, a classical pattern recognition problem. The objective of this study is to improve the recognition rates by combining different representations of non-normalized handwritten digits. These features include some global statistics, moments, profile and projection based features and features computed from the contour and skeleton of the digits. Some of these features are extracted from the complete image of digit while others are extracted from different regions of the image by first applying a uniform grid sampling to the image. Classification is carried out using one-against-all SVM. The experiments conducted on the CVL Single Digit Database realized high recognition rates which are comparable to state-of-the-art methods on this subject.

Segmentation and Recognition Strategy of Handwritten Connected Digits Based on the Oriented Sliding Window

In this paper, we propose a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-recognition of handwritten connected digits based on the oriented sliding window. The proposed approach allows separating adjacent digits according the connection configuration by finding at the same time the interconnection points between adjacent digits and the cutting path. The segmentation-recognition using the global decision module allows the rejection or acceptance of the processed image. Experimental results conducted on the handwritten digit database NIST SD19 show the effective use of the sliding window for segmentation-recognition.

Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs)

Abstract Classification of gender from images of handwriting is an interesting research problem in computerized analysis of handwriting. The correlation between handwriting and gender of writer can be exploited to develop intelligent systems to facilitate forensic experts, document examiners, paleographers, psychologists and neurologists. We propose a handwriting based gender recognition system that exploits texture as the discriminative attribute between male and female handwriting. The textural information in handwriting is captured using combinations of different configurations of oriented Basic Image Features (oBIFs). oBIFs histograms and oBIFs columns histograms extracted from writing samples of male and female handwriting are used to train a Support Vector Machine classifier (SVM). The system is evaluated on three subsets of the QUWI database of Arabic and English writing samples using the experimental protocols of the ICDAR 2013, ICDAR 2015 and ICFHR 2016 gender classification competitions reporting classification rates of 71%, 76% and 68% respectively; outperforming the participating systems of these competitions. While textural measures like local binary patterns, histogram of oriented gradients and Gabor filters etc. have remained a popular choice for many expert systems targeting recognition problems, the present study demonstrates the effectiveness of relatively less investigated oBIFs as a robust textual descriptor.

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.

Oriented Basic Image Features Column for isolated handwritten digit

Several approaches for handwritten digits recognition are proposed an appearance feature-based approach. In this paper we process handwritten digit image without deskewing using oriented Basic Image Features (oBIF) Column scheme extracted from the complete image as well as from different regions of the image by applying a uniform grid sampling to the image. oBIF Column scheme is a very efficient feature descriptor for handwritten digits which is arise from variations in size, shape and slant. Moreover, 4th Nearest Neighbor (4-NN) has been employed as classifier which has better responses. The experimental study is conducted on MNIST dataset and 98.32% recognition rate has been achieved which is comparable with the state of the art.

ICFHR2016 Competition on Multi-script Writer Demographics Classification Using "QUWI" Database

This competition is aimed at classification of writer demographics from offline handwritten documents using the QUWI database. QUWI is a bilingual database comprising writing samples of same individuals in Arabic and English. This allows evaluating the performance of different systems in a more challenging multi-script environment. This paper presents the details of the competition tasks, the datasets used in each of the tasks, a brief description of the participating systems, experimental protocol and evaluation criteria and finally the overall rankings of the participants.