Fouad Slimane

A New Arabic Printed Text Image Database and Evaluation Protocols

We report on the creation of a database composed of images of Arabic Printed words. The purpose of this database is the large-scale benchmarking of open-vocabulary, multi-font, multi-size and multi-style text recognition systems in Arabic. The challenges that are addressed by the database are in the variability of the sizes, fonts and style used to generate the images. A focus is also given on low-resolution images where anti-aliasing is generating noise on the characters to recognize. The database is synthetically generated using a lexicon of 113’284 words, 10 Arabic fonts, 10 font sizes and 4 font styles. The database contains 45’313’600 single word images totaling to more than 250 million characters. Ground truth annotation is provided for each image. The database is called APTI for Arabic Printed Text Images.

A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution

In this paper, we propose a new font and size identification method for ultra-low resolution Arabic word images using a stochastic approach. The literature has proved the difficulty for Arabic text recognition systems to treat multi-font and multi-size word images. This is due to the variability induced by some font family, in addition to the inherent difficulties of Arabic writing including cursive representation, overlaps and ligatures. This research work proposes an efficient stochastic approach to tackle the problem of font and size recognition. Our method treats a word image with a fixed-length, overlapping sliding window. Each window is represented with a 102 features whose distribution is captured by Gaussian Mixture Models (GMMs). We present three systems: (1) a font recognition system, (2) a size recognition system and (3) a font and size recognition system. We demonstrate the importance of font identification before recognizing the word images with two multi-font Arabic OCRs (cascading and global). The cascading system is about 23% better than the global multi-font system in terms of word recognition rate on the Arabic Printed Text Image (APTI) database which is freely available to the scientific community.

Duration Models for Arabic Text Recognition Using Hidden Markov Models

We present in this paper a system for recognition of printed Arabic text based on hidden Markov models (HMM). While HMMs have been successfully used in the past for such a task, we report here on significant improvements of the recognition performance with the introduction of minimum and maximum duration models. The improvements allow us to build a system working in open vocabulary mode, i.e., without any limitations on the size of the vocabulary. The evaluation of our system is performed using HTK (hidden Markov model toolkit) on a database of word images that are synthetically generated.

ICDAR 2011 - Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text

This paper describes the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text held in the context of the 11

Gaussian Mixture Models for Arabic Font Recognition

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Impact of Character Models Choice on Arabic Text Recognition Performance

International Conference on Document Analysis and Recognition (ICDAR2011), during September 18-21, 2011, Beijing, China. This first competition used the freely available Arabic Printed Text Image (APTI) database. Several research groups have started using the APTI database and this year, 2 groups with 3 systems are participating in the competition. The systems are compared using the recognition rates at the character and word levels. The systems were tested on one test dataset which is unknown to all participants (set 6 of APTI database). The systems are compared on the most important characteristic of classification systems, the recognition rate. A short description of the participating groups, their systems, the experimental setup, and the observed results are presented.

A New Text-Independent GMM Writer Identification System Applied to Arabic Handwriting

We present in this paper a new approach for Arabic font recognition. Our proposal is to use a fixed-length sliding window for the feature extraction and to model feature distributions with Gaussian Mixture Models (GMMs). This approach presents a double advantage. First, we do not need to perform a priori segmentation into characters, which is a difficult task for arabic text. Second, we use versatile and powerful GMMs able to model finely distributions of features in large multi-dimensional input spaces. We report on the evaluation of our system on the APTI (Arabic Printed Text Image) database using 10 different fonts and 10 font sizes. Considering the variability of the different font shapes and the fact that our system is independent of the font size, the obtained results are convincing and compare well with competing systems.

Affixal Approach versus Analytical Approach for Off-Line Arabic Decomposable Vocabulary Recognition

We analyze in this paper the impact of sub-models choice for automatic Arabic printed text recognition based on Hidden Markov Models (HMM). In our approach, sub-models correspond to characters shapes assembled to compose words models. One of the peculiarities of Arabic writing is to present various character shapes according to their position in the word. With 28 basic characters, there are over 120 different shapes. Ideally, there should be one sub model for each different shape. However, some shapes are less frequent than others and, as training databases are finite, the learning process leads to less reliable models for the infrequent shapes. We show in this paper that an optimal set of models has then to be found looking for the trade-off between having more models capturing the intricacies of shapes and grouping the models of similar shapes with other. We propose in this paper different sets of sub-models that have been evaluated using the Arabic Printed Text Image (APTI) Database freely available for the scientific community.

ICFHR2014 Competition on Arabic Writer Identification Using AHTID/MW and KHATT Databases

This paper proposes a system for text-independent writer identification based on Arabic handwriting using only 21 features. Gaussian Mixture Models (GMMs) are used as the core of the system. GMMs provide a powerful representation of the distribution of features extracted using a fixed-length sliding window from the text lines and words of a writer. For each writer a GMM is built and trained using words and text lines images of that writer. At the recognition phase, the system returns log-likelihood scores. The GMM model(s) with the highest score(s) is (are) selected depending if the score is computed in Top-1 or Top-n level. Experiments using only word and text line images from the freely available Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) demonstrate a good performance for the Top-1, Top-2, Top-5 and Top-10 results.

Local Binary Patterns for Arabic Optical Font Recognition

In this paper, we propose a comparative study between the affixal approach and the analytical approach for off-line Arabic decomposable word recognition. The analytical approach is based on the modeling of alphabetical letters. The affixad approach is based on the modeling of the linguistic entity namely prefix, infix, suffix and root. The experimental results obtained by these two last approaches are presented on the basis of the printed decomposable word data set in mono-font nature by varying the character sizes. We achieve then our paper by the current improvements of our works concerning the Arabic multi-font, multi-style and multi-size word recognition.