End-Shape Analysis for Automatic Segmentation of Arabic Handwritten Texts

Word segmentation is an important task for many methods that are related to document understanding especially word spotting and word recognition. Several approaches of word segmentation have been proposed for Latin-based languages while a few of them have been introduced for Arabic texts. The fact that Arabic writing is cursive by nature and unconstrained with no clear boundaries between the words makes the processing of Arabic handwritten text a more challenging problem. In this thesis, the design and implementation of an End-Shape Letter (ESL) based segmentation system for Arabic handwritten text is presented. This incorporates four novel aspects: (i) removal of secondary components, (ii) baseline estimation, (iii) ESL recognition, and (iv) the creation of a new off-line CENPARMI ESL database. Arabic texts include small connected components, also called secondary components. Removing these components can improve the performance of several systems such as baseline estimation. Thus, a robust method to remove secondary components that takes into consideration the challenges in the Arabic handwriting is introduced. The methods reconstruct the image based on some criteria. The results of this method were subsequently compared with those of two other methods that used the same database. The results show that the proposed method is effective. Baseline estimation is a challenging task for Arabic texts since it includes ligature, overlapping, and secondary components. Therefore, we propose a learning-based approach that addresses these challenges. Our method analyzes the image and extracts baseline dependent features. Then, the baseline is estimated using a classifier. Algorithms dealing with text segmentation usually analyze the gaps between connected components. These algorithms are based on metric calculation, finding threshold, and/or gap classification. We use two well-known metrics: bounding box and convex hull to test metric-based method on Arabic handwritten texts, and to include this technique in our approach. To determine the threshold, an unsupervised learning approach, known as the Gaussian Mixture Model, is used. Our ESL-based segmentation approach extracts the final letter of a word using rule-based technique and recognizes these letters using the implemented ESL classifier. To demonstrate the benefit of text segmentation, a holistic word spotting system is implemented. For this system, a word recognition system is implemented. A series of experiments with different sets of features are conducted. The system shows promising results

Learning-Based Arabic Word Spotting Using a Hierarchical Classifier

The effective retrieval of information from scanned and written documents is becoming essential with the increasing amounts of digitized documents, and therefore developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Such systems have been implemented for Latin-based and Chinese languages, while few of them have been implemented for Arabic handwriting. The fact that Arabic writing is cursive by nature and unconstrained, with no clear white space between words, makes the processing of Arabic handwritten documents a more challenging problem. In this thesis, the design and implementation of a learning-based Arabic handwritten word spotting system is presented. This incorporates the aspects of text line extraction, handwritten word recognition, partial segmentation of words, word spotting and finally validation of the spotted words. The Arabic text line is more unconstrained than that of other scripts, essentially since it also includes small connected components such as dots and diacritics that are usually located between lines. Thus, a robust method to extract text lines that takes into consideration the challenges in the Arabic handwriting is proposed. The method is evaluated on two Arabic handwritten documents databases, and the results are compared with those of two other methods for text line extraction. The results show that the proposed method is effective, and compares favorably with the other methods. Word spotting is an automatic process to search for words within a document. Applying this process to handwritten Arabic documents is challenging due to the absence of a clear space between handwritten words. To address this problem, an effective learning-based method for Arabic handwritten word spotting is proposed and presented in this thesis. For this process, sub-words or pieces of Arabic words form the basic components of the search process, and a hierarchical classifier is implemented to integrate statistical language models with the segmentation of an Arabic text line into sub-words. The holistic and analytical paradigms (for word recognition and spotting) are studied, and verification models based on combining these two paradigms have been proposed and implemented to refine the outcomes of the analytical classifier that spots words. Finally, a series of evaluation and testing experiments have been conducted to evaluate the effectiveness of the proposed systems, and these show that promising results have been obtained