Abdelaali Hassaine

The ICDAR2011 Arabic Writer Identification Contest

Arabic writer identification is a very active research field. However, no standard benchmark is available for researchers in this field. The aim of this competition is to gather researchers and compare recent advances in Arabic writer identification. This competition was hosted by Kaggle, it has attracted thirty participants from both academia and industry. This paper gives details on this competition, including the evaluation procedure, description of participating methods and their performances.

Automatic prediction of age, gender, and nationality in offline handwriting

The classification of handwriting into different categories, such as age, gender, and nationality, has several applications. In forensics, handwriting classification helps investigators focus on a certain category of writers. However, only a few studies have been carried out in this field. Classification of handwriting into a demographic category is generally performed in two steps: feature extraction and classification. The performance of a system depends mainly on the feature extraction step because characterizing features makes it possible to distinguish between writers. In this study, we propose several geometric features to characterize handwritings and use these features to perform the classification of handwritings with regards to age, gender, and nationality. Features are combined using random forests and kernel discriminant analysis. Classification rates are reported on the QUWI dataset, reaching 74.05% for gender prediction, 55.76% for age range prediction, and 53.66% for nationality prediction when all writers produce the same handwritten text and 73.59% for gender prediction, 60.62% for age range prediction, and 47.98% for nationality prediction when each writer produces different handwritten text.The classification of handwriting into different categories, such as age, gender, and nationality, has several applications. In forensics, handwriting classification helps investigators focus on a certain category of writers. However, only a few studies have been carried out in this field. Classification of handwriting into a demographic category is generally performed in two steps: feature extraction and classification. The performance of a system depends mainly on the feature extraction step because characterizing features makes it possible to distinguish between writers. In this study, we propose several geometric features to characterize handwritings and use these features to perform the classification of handwritings with regards to age, gender, and nationality. Features are combined using random forests and kernel discriminant analysis. Classification rates are reported on the QUWI dataset, reaching 74.05% for gender prediction, 55.76% for age range prediction, and 53.66% for nationality prediction when all writers produce the same handwritten text and 73.59% for gender prediction, 60.62% for age range prediction, and 47.98% for nationality prediction when each writer produces different handwritten text.

Automatic handedness detection from off-line handwriting

In forensics, the handedness detection or the classification of writers into left or right-handed helps investigators focusing more on a certain category of suspects. However, only a few studies have been carried out in this field. Classification of handwriting into a demographic category is generally performed in two steps: feature extraction and classification. In this study, we propose a system which extract characterizing features from handwritings and use those features to perform the classification of handwritings with regards to handedness. Classification rates are reported on the QUWI dataset, reaching almost 70% for Left and right Handwriting.

Text Categorization Using Hyper Rectangular Keyword Extraction: Application to News Articles Classification

Automatic text categorization is still a very important research topic. Typical applications include assisting end-users in archiving existing documents, or helping them in browsing existing corpus of documents in a hierarchical way. Text categorization is usually composed of two main steps: keyword extraction and classification. In this paper, a corpus of documents is represented by a binary relation linking each document to the words it contains. From this relation, the Hyper Rectangle Algorithm extracts the list of the most representative words in a hierarchical way. A hyper-Rectangle associated to an element of the range of a binary relation is the union of all non-enlargeable rectangles containing it. The extracted keywords are fed into the random forest classifier in order to predict the category of each document. The method has been validated on the popular Reuters 21578 news articles database. Results are very promising and show the effectiveness of the Hyper Rectangular method in extracting relevant keywords.

Machine Learning and Conceptual Reasoning for Inconsistency Detection

This paper focuses on detecting inconsistencies within text corpora. It is a very interesting area with many applications. Most existing methods deal with this problem using complicated textual analysis, which is known for not being accurate enough. We propose a new methodology that consists of two steps, the first one being a machine learning step that performs multilevel text categorization. The second one applies conceptual reasoning on the predicted categories in order to detect inconsistencies. This paper has been validated on a set of Islamic advisory opinions (also known as fatwas). This domain is gaining a large interest with users continuously checking the authenticity and relevance of such content. The results show that our method is very accurate and can complement existing methods using the linguistic analysis.

Using conceptual reasoning for inconsistencies detection in Islamic advisory opinion (Fatwas)

The Islamic websites play an important role in disseminating Islamic knowledge and information about Islamic ruling. Their number and the content they provide is continuously increasing which require in-depth investigations in content evaluation automation. In this paper, we are proposing the use of conceptual reasoning for detecting inconsistencies in case of Fatwas evaluation. Inconsistencies are detected from propositional logic point-of-view based on Truth table binary relation.

Hyper rectangular trend analysis application to Islamic rulings (fatwas)

Trend analysis is a research field with a large number of applications ranging from monitoring potential rivals to analyzing interests of a certain category of people. Applying trend analysis to the Islamic domain makes it possible to have a general idea about topics discussed by Muslims all over the world. It helps both scholars and social science researchers understanding the needs and the interest domains of each Muslim society. In this paper, we present a trend analysis method based on hyper-concepts. Hyper-concepts make it possible to decompose any corpus into non-overlapping rectangular relations and to highlight the most representative attributes or keywords. We illustrate the effectiveness of our method in identifying relevant keywords related to the Islamic context and we show how to use our method for identifying trending topics with respect to time.

Using codebooks generated from text skeletonization for forensic writer identification

In this paper, we propose a novel approach for writer identification using codebook generation based on text skeletonization.Unlike other schemes, the skeleton in this approach is segmented at its junction pixels into elementary graphic units called graphemes. The codebook is generated by clustering the graphemes according to their distributions into a predefined grid. This method has been evaluated using the benchmarking dataset of the International Conference on Document Analysis and Recognition (ICDAR 2011) writer identification contest and has shown promising results. We also studied the effect of the amount of handwriting on the identification accuracy of the method and demonstrated that the proposed method is valid for Latin and Greek languages.

Offline Biometric Signature Verification Using Geometric and Colour Features

Offline signature verification is undeniably a prominent aspect of the biometric research. It has many applications, including banking and forensics. Signature verification task involves a comparison of questioned signature with a set of one or more reference signatures. The questioned signature may be genuine (written by the authentic writer), a forgery (written by a different person) or a disguise (written by the authentic writer with some modifications with the intent of a later denial). Signature verification generally encompasses the two main steps: feature extraction and classification. The system performance is primarily dependent on the feature extraction step because the characterising features distinguish between genuine, disguised and forged signatures. In this study, we propose several geometric and colour features to characterise the signatures. The features are combined using random forests, logistic regression and generalised linear models. The results are reported on the datasets of several competitions, including ICDAR 2009, ICFHR 2010, ICDAR 2011 and ICFHR 2012 signature verification competitions. The proposed method generally outperforms the other participating methods.

Authenticity detection as a binary text categorization problem: Application to Hadith authentication

Authentication of Hadiths (sayings of Prophet Muhammad) is very important field for religious scholars as well as historians. Authenticity verification is traditionally conducted by studying how trustworthy is each person in the narration chain. In this study, we propose a novel approach completely based on the content of each Hadith. For each category of hadiths (authentic and non-authentic), we create a binary relation in which the hadiths correspond to the objects of the relation and the words correspond to its attributes. Keywords for each category are then obtained in a hierarchical ordering of importance using the hyper rectangular decomposition. Classification is done by feeding the extracted keywords to a logistic regression classifier. The method has been validated on a database of about 1600 hadiths. Results show that classification accuracy increases with the number of annotators who agreed on the authenticity of each hadith. These findings suggest that our method successfully extracts relevant keywords and can be combined with other traditional methods.