Doctoral Thesis Defense: Amani Jamal

Speaker: Amani Jamal

Supervisor: Dr. C. Y. Suen

Examining Committee: Drs. N. Kharma, L. Lam, R. Witte, M. Ahmadi, C. Wang (Chair)

Title:  End-shape Analysis for Automatic Segmentation of Arabic Handwritten Texts

Date: Thursday, July 30, 2015

Time: 10:00 a.m.

Place: EV 1.162

ABSTRACT

Word segmentation is an important task for many methods that are related to document understanding especially word spotting and word recognition. In addition, the accuracy of these systems affects the performance of many applications, such as translation, scoring and categorizing. 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) recognition-based segmentation system for Arabic handwritten text is presented. This incorporates four novel aspects: (i) removal of secondary components using Morphological Reconstruction, (ii) Learning-based baseline estimation, (iii) ESL-based segmentation, and (iv) the creation of a new off-line CENPARMI ESL database.

Removing secondary components can improve the performance of several systems such as baseline estimation, and skew correction. Our method reconstruct the image based on some criteria. The results show that the proposed method is effective.

Baseline estimation is an essential pre-processing step for many methods related to recognition systems. We propose a learning-based approach that addresses many 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 call these algorithms metric-based segmentation technique. 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.