Wasfi G. Al-Khatib

License plate recognition system

License Plate recognition (LPR) system is a key to many traffic related applications such as road traffic monitoring or parking lots access control. This paper proposes an automatic license plate recognition system for Saudi Arabian license plates. The system presents an algorithm for the extraction of license plate and segmentation of characters. Recognition is done using template matching. However the proposed work seems to be the first attempt towards the recognition of Saudi Arabian license plates. The performance of the system has been investigated on real images of about 710 vehicles captured under various illumination conditions. Recognition of about 96% shows that the system is quite efficient.

KHATT: An open Arabic offline handwritten text database

Abstract A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier. The database is made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, forms analysis, pre-processing, segmentation. Several international research groups/researchers acquired the database for use in their research so far.

An approach to offline Arabic character recognition using neural networks

Character recognition system can contribute tremendously towards the advancement of automation process and can be useful in many other applications such as Data Entry, Check Verification etc. This paper presents a technique for the automatic recognition of Arabic Characters. The technique is based on Neural Pattern Recognition Approach. The main features of the system are preprocessing of the text, segmentation of the text to individual characters, Feature extraction using centralized moments technique and recognition using RBF Network. The system is implemented in Java Programming Language under Windows Environment. The System is designed for a single font multi size character set.

KHATT: Arabic Offline Handwritten Text Database

In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) after completion of the collection of 1000 handwritten forms written by 1000 writers from different countries. It is composed of an image database containing images of the written text at 200, 300, and 600 dpi resolutions, a manually verified ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. Preliminary experiments on Arabic handwritten text recognition are conducted using sample data from the database and the results are reported. The database will be made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, etc.

Machine-learning based classification of speech and music

The need to classify audio into categories such as speech or music is an important aspect of many multimedia document retrieval systems. In this paper, we investigate audio features that have not been previously used in music-speech classification, such as the mean and variance of the discrete wavelet transform, the variance of Mel-frequency cepstral coefficients, the root mean square of a lowpass signal, and the difference of the maximum and minimum zero-crossings. We, then, employ fuzzy C-means clustering to the problem of selecting a viable set of features that enables better classification accuracy. Three different classification frameworks have been studied:Multi-Layer Perceptron (MLP) Neural Networks, radial basis functions (RBF) Neural Networks, and Hidden Markov Model (HMM), and results of each framework have been reported and compared. Our extensive experimentation have identified a subset of features that contributes most to accurate classification, and have shown that MLP networks are the most suitable classification framework for the problem at hand.

An Arabic handwriting synthesis system

Abstract In this paper, we present an Arabic handwriting synthesis system. Two concatenation models to synthesize Arabic words from segmented characters are adopted: Extended-Glyphs connection and Synthetic-Extensions connection. We use our system to synthesize handwriting from a collected dataset and inject it into an expanded dataset. We experiment by training a state-of-the-art Arabic handwriting recognition system on the collected dataset, as well as on the expanded dataset, and test it on the IFN/ENIT Arabic benchmark dataset. We show significant improvement in recognition performance due to the data that was synthesized by our system.

Computer Aided Indexing of Historical Manuscripts

Arabic manuscripts represent a rich source of knowledge that has been highly underutilized. Huge repositories of historical artifacts are yet to be typeset and published in book-form. Given vast content of these manuscripts, it is important to develop indexing systems that support content-based retrieval from historical manuscripts. In this paper, we propose a computer aided retrieval and indexing system for Arabic historical manuscripts. The proposed system extracts meaningful information (features) that is used in indexing. Some preprocessing steps are also implemented in order to enhance the quality of document images. More than one form of a similarity measure has been tested. The developed prototype system has shown encouraging results with respect to the word matching rates achieved

Automatic classification of speech and music using neural networks

The importance of automatic discrimination between speech signals and music signals has evolved as a research topic over recent years. The need to classify audio into categories such as speech or music is an important aspect of many multimedia document retrieval systems. Several approaches have been previously used to discriminate between speech and music data. In this paper, we propose the use of the mean and variance of the discrete wavelet transform in addition to other features that have been used previously for audio classification. We have used Multi-Layer Perceptron (MLP) Neural Networks as a classifier. Our initial tests have shown encouraging results that indicate the viability of our approach.

Cross-word Arabic pronunciation variation modeling for speech recognition

One of the problems in the speech recognition of Modern Standard Arabic (MSA) is the cross-word pronunciation variation. Cross-word pronunciation variations alter the phonetic spelling of words beyond their listed forms in the phonetic dictionary, leading to a number of Out-Of-Vocabulary (OOV) wordforms. This paper presents a knowledge-based approach to model cross-word pronunciation variation at both phonetic dictionary and language model levels. The proposed approach is based on modeling cross-word pronunciation variation by expanding the phonetic dictionary and corpus transcription. The Baseline system contains a phonetic dictionary of 14,234 words from a 5.4 hours corpus of Arabic broadcast news. The expanded dictionary contains 15,873 words. Also, the corpus transcription is expanded according to the applied Arabic phonological rules. Using Carnegie Mellon University (CMU) Sphinx speech recognition engine, the Enhanced system achieved Word Error Rate (WER) of 9.91% on a test set of fully discretized transcription of about 1.1 hours of Arabic broadcast news. The WER is enhanced by 2.3% compared to the Baseline system.

Detection of Questions in Arabic Audio Monologues Using Prosodic Features

Prosody has been widely used in many speech-related applications including speaker and word recognition, emotion and accent identification, topic and sentence segmentation, and text-to-speech applications. An important application we investigate is that of identifying question sentences in Arabic monologue lectures. Languages other than Arabic have received a lot of attention in this regard. We approach this problem by first segmenting the sentences from the continuous speech using intensity and duration features. Prosodic features are, then, extracted from each sentence. These features are used as input to decision trees to classify each sentence into either question or non question sentence. Our results suggest that questions are cued by more than one type of prosodic features in natural Arabic speech. We used C4.5 decision trees for classification and achieved 75.7% accuracy. Feature specific analysis further reveals that energy and fundamental frequency features are mainly responsible for discriminating between questions and non-question sentences.