Survey and bibliography of Arabic optical text recognition

The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.

Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition

In this paper, we present a review of the state of the art in the current classification techniques used in the optical character recognition of the Arabic script (AOCR). We consider multiple sources of information-based hybrid approaches and multiple classifiers. We show that in order to improve classification results obtained with single classifiers, it is necessary to combine several sources of information either at the level of feature extraction/description, or at the classification stage, or at both levels. We provide a qualitative comparison and discuss the strengths and weaknesses of these approaches.

Classification of Arabic script using multiple sources of information: State of the art and perspectives

Segmentation and recognition of Arabic characters by structural classification

Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates using blob labeling and clustering for segmentation, seven popular and one proposed edge detectors for feature extraction and neural networks for classification. There were eight experiments conducted using eight different edge detectors: Kirsch, Sobel, Laplacian, Wallis, Prewitt, Frei Chen and a proposed edge detector. The result had shown kirsch edge detectors is the best technique for feature exractor while the proposed achieved better results compared to Prewitt, Frei Chen and Wallis

Talaat S. El-Sheikh

Papers

Automatic recognition of isolated Arabic characters