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No / This paper describes a novel procedure for thinning binary text images by generating graphical representations of words within the image. A smoothed polygonal approximation of the boundaries of each word is first decomposed into a set of contiguous triangles. Each triangle is then classified into one of only three possible types from which a graph is generated that represents the topological features of the object. Joining graph points with straight lines generates a final polygon skeleton that, by construction, is one pixel wide and fully connected. Results of applying the procedure to thinning Arabic and English handwriting are presented. Comparisons of skeleton structure and execution time with results from alternative techniques are also presented. The procedure is considerably faster than the alternatives tested when the image resolution is greater than 600 dpi and the graphical representation often needed in subsequent recognition steps is available without further processing.

Magister Artium - MA / The main focus of this dissertation is a critical study of the Arabic text, titled: Lubāb al ‘Ilm Fī Sūrah al-Najm (The Kernel of Knowledge in the Chapter of the Star) by Shaykh Ahmad bin Muṣṭafā’ al-‘Alāwī. Due to the lack of research on esoteric commentaries of the Qurʾān in the English language, there is a need to embark upon an in-depth study of such texts. An important work on Shaykh al-‘Alāwī in English is Martin Lings’ A Sufi Saint of the Twentieth Century. This book is an excellent introduction to the life, works and thought of Shaykh al-‘Alāwī, but it does not deal with a specific text in any detail. Thus, the purpose of this dissertation is to examine closely the above-mentioned text of Shaykh al-‘Alāwī as a sample of his esoteric interpretation of the Qurʾān. For the purpose of this thesis, I shall undertake a translation of his exegesis (tafsīr) on Sūrah al-Najm (the Chapter of the Star). I will include with it explanatory notes and identification of key quotations and sources. This sample from Shaykh al-‘Alāwī’s work will form the basis of my critical analyses. It will also provide a means for comparison with some of his other works, and with Qurʾānic commentaries of the same genre by other Sufi scholars, both classical and modern. In this dissertation, I also seek to offer some answers and proofs concerning the validity of the existence of esoteric tafsīr and why it is needed. I will do this by examining key verses in the Qur’ān and the Sunnah (traditions of the Prophet Muḥammad). The distinction between esoteric and exoteric interpretations of the Qurʾān will also be dealt with in this dissertation. Although the emphasis will be on the esoteric dimension, neither the esoteric nor the exoteric dimension will be treated in a mutually exclusive way. Most Arabic commentaries on the Qurʾān tend towards the exoteric and literal meanings of the text, but the exoteric form also has an inner dimension which Shaykh al-‘Alāwī demonstrates in his commentary on Sūrah al-Najm.

The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved.

The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved.

yes / This paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256). Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts. The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages.

Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired people and understanding filled forms. This research work aims to contribute to the current research in the field of optical character recognition (OCR) of printed Arabic text by developing novel techniques and schemes to advance the performance of the state of the art Arabic OCR systems. Statistical and analytical analysis for Arabic Text was carried out to estimate the probabilities of occurrences of Arabic character for use with Hidden Markov models (HMM) and other techniques. Since there is no publicly available dataset for printed Arabic text for recognition purposes it was decided to create one. In addition, a minimal Arabic script is proposed. The proposed script contains all basic shapes of Arabic letters. The script provides efficient representation for Arabic text in terms of effort and time. Based on the success of using HMM for speech and text recognition, the use of HMM for the automatic recognition of Arabic text was investigated. The HMM technique adapts to noise and font variations and does not require word or character segmentation of Arabic line images. In the feature extraction phase, experiments were conducted with a number of different features to investigate their suitability for HMM. Finally, a novel set of features, which resulted in high recognition rates for different fonts, was selected. The developed techniques do not need word or character segmentation before the classification phase as segmentation is a byproduct of recognition. This seems to be the most advantageous feature of using HMM for Arabic text as segmentation tends to produce errors which are usually propagated to the classification phase. Eight different Arabic fonts were used in the classification phase. The recognition rates were in the range from 98% to 99.9% depending on the used fonts. As far as we know, these are new results in their context. Moreover, the proposed technique could be used for other languages. A proof-of-concept experiment was conducted on English characters with a recognition rate of 98.9% using the same HMM setup. The same techniques where conducted on Bangla characters with a recognition rate above 95%. Moreover, the recognition of printed Arabic text with multi-fonts was also conducted using the same technique. Fonts were categorized into different groups. New high recognition results were achieved. To enhance the recognition rate further, a post-processing module was developed to correct the OCR output through character level post-processing and word level post-processing. The use of this module increased the accuracy of the recognition rate by more than 1%.

Cette thèse s'intéresse à la détection et la reconnaissance du texte arabe incrusté dans les vidéos. Dans ce contexte, nous proposons différents prototypes de détection et d'OCR vidéo (Optical Character Recognition) qui sont robustes à la complexité du texte arabe (différentes échelles, tailles, polices, etc.) ainsi qu'aux différents défis liés à l'environnement vidéo et aux conditions d'acquisitions (variabilité du fond, luminosité, contraste, faible résolution, etc.). Nous introduisons différents détecteurs de texte arabe qui se basent sur l'apprentissage artificiel sans aucun prétraitement. Les détecteurs se basent sur des Réseaux de Neurones à Convolution (ConvNet) ainsi que sur des schémas de boosting pour apprendre la sélection des caractéristiques textuelles manuellement conçus. Quant à notre méthodologie d'OCR, elle se passe de la segmentation en traitant chaque image de texte en tant que séquence de caractéristiques grâce à un processus de scanning. Contrairement aux méthodes existantes qui se basent sur des caractéristiques manuellement conçues, nous proposons des représentations pertinentes apprises automatiquement à partir des données. Nous utilisons différents modèles d'apprentissage profond, regroupant des Auto-Encodeurs, des ConvNets et un modèle d'apprentissage non-supervisé, qui génèrent automatiquement ces caractéristiques. Chaque modèle résulte en un système d'OCR bien spécifique. Le processus de reconnaissance se base sur une approche connexionniste récurrente pour l'apprentissage de l'étiquetage des séquences de caractéristiques sans aucune segmentation préalable. Nos modèles d'OCR proposés sont comparés à d'autres modèles qui se basent sur des caractéristiques manuellement conçues. Nous proposons, en outre, d'intégrer des modèles de langage (LM) arabes afin d'améliorer les résultats de reconnaissance. Nous introduisons différents LMs à base des Réseaux de Neurones Récurrents capables d'apprendre des longues interdépendances linguistiques. Nous proposons un schéma de décodage conjoint qui intègre les inférences du LM en parallèle avec celles de l'OCR tout en introduisant un ensemble d’hyper-paramètres afin d'améliorer la reconnaissance et réduire le temps de réponse. Afin de surpasser le manque de corpus textuels arabes issus de contenus multimédia, nous mettons au point de nouveaux corpus manuellement annotés à partir des flux TV arabes. Le corpus conçu pour l'OCR, nommé ALIF et composée de 6,532 images de texte annotées, a été publié a des fins de recherche. Nos systèmes ont été développés et évalués sur ces corpus. L’étude des résultats a permis de valider nos approches et de montrer leurs efficacité et généricité avec plus de 97% en taux de détection, 88.63% en taux de reconnaissance mots sur le corpus ALIF dépassant ainsi un des systèmes d'OCR commerciaux les mieux connus par 36 points. / This thesis focuses on Arabic embedded text detection and recognition in videos. Different approaches robust to Arabic text variability (fonts, scales, sizes, etc.) as well as to environmental and acquisition condition challenges (contrasts, degradation, complex background, etc.) are proposed. We introduce different machine learning-based solutions for robust text detection without relying on any pre-processing. The first method is based on Convolutional Neural Networks (ConvNet) while the others use a specific boosting cascade to select relevant hand-crafted text features. For the text recognition, our methodology is segmentation-free. Text images are transformed into sequences of features using a multi-scale scanning scheme. Standing out from the dominant methodology of hand-crafted features, we propose to learn relevant text representations from data using different deep learning methods, namely Deep Auto-Encoders, ConvNets and unsupervised learning models. Each one leads to a specific OCR (Optical Character Recognition) solution. Sequence labeling is performed without any prior segmentation using a recurrent connectionist learning model. Proposed solutions are compared to other methods based on non-connectionist and hand-crafted features. In addition, we propose to enhance the recognition results using Recurrent Neural Network-based language models that are able to capture long-range linguistic dependencies. Both OCR and language model probabilities are incorporated in a joint decoding scheme where additional hyper-parameters are introduced to boost recognition results and reduce the response time. Given the lack of public multimedia Arabic datasets, we propose novel annotated datasets issued from Arabic videos. The OCR dataset, called ALIF, is publicly available for research purposes. As the best of our knowledge, it is first public dataset dedicated for Arabic video OCR. Our proposed solutions were extensively evaluated. Obtained results highlight the genericity and the efficiency of our approaches, reaching a word recognition rate of 88.63% on the ALIF dataset and outperforming well-known commercial OCR engine by more than 36%.

Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired people and understanding filled forms. This research work aims to contribute to the current research in the field of optical character recognition (OCR) of printed Arabic text by developing novel techniques and schemes to advance the performance of the state of the art Arabic OCR systems. Statistical and analytical analysis for Arabic Text was carried out to estimate the probabilities of occurrences of Arabic character for use with Hidden Markov models (HMM) and other techniques. Since there is no publicly available dataset for printed Arabic text for recognition purposes it was decided to create one. In addition, a minimal Arabic script is proposed. The proposed script contains all basic shapes of Arabic letters. The script provides efficient representation for Arabic text in terms of effort and time. Based on the success of using HMM for speech and text recognition, the use of HMM for the automatic recognition of Arabic text was investigated. The HMM technique adapts to noise and font variations and does not require word or character segmentation of Arabic line images. In the feature extraction phase, experiments were conducted with a number of different features to investigate their suitability for HMM. Finally, a novel set of features, which resulted in high recognition rates for different fonts, was selected. The developed techniques do not need word or character segmentation before the classification phase as segmentation is a byproduct of recognition. This seems to be the most advantageous feature of using HMM for Arabic text as segmentation tends to produce errors which are usually propagated to the classification phase. Eight different Arabic fonts were used in the classification phase. The recognition rates were in the range from 98% to 99.9% depending on the used fonts. As far as we know, these are new results in their context. Moreover, the proposed technique could be used for other languages. A proof-of-concept experiment was conducted on English characters with a recognition rate of 98.9% using the same HMM setup. The same techniques where conducted on Bangla characters with a recognition rate above 95%. Moreover, the recognition of printed Arabic text with multi-fonts was also conducted using the same technique. Fonts were categorized into different groups. New high recognition results were achieved. To enhance the recognition rate further, a post-processing module was developed to correct the OCR output through character level post-processing and word level post-processing. The use of this module increased the accuracy of the recognition rate by more than 1%. / King Fahd University of Petroleum and Minerals (KFUPM)