Structural analysis of Arabic handwriting: segmentation and recognition

In this paper, a structural method of recognising Arabic handwritten characters is proposed. The major problem in cursive text recognition is the segmentation into characters or into representative strokes. When we segment the cursive portions of words, we take into account the contextual properties of the Arabic grammar and the junction segments connecting the characters to each other along the writing line. The problem of overlapping characters is resolved with a contour-following algorithm associated with the labelling of the detected contours. In the recognition phase, the characters are gathered into ten families of candidate characters with similar shapes. Then a heterarchical analysis follows that checks the pattern via goal-directed feedback control.     

Improving the DBLSTM for on-line Arabic handwriting recognition

Multimedia Tools and Applications - Various applications involved in the computer recognition of pen-input handwritten words, such as the online form filling, text editing, note taking, and so on....     

A large vocabulary system for Arabic online handwriting recognition

The success of using Hidden Markov Models (HMMs) for speech recognition application has motivated the adoption of these models for handwriting recognition especially the online handwriting that has large similarity with the speech signal as a sequential process. Some languages such as Arabic, Farsi and Urdo include large number of delayed strokes that are written above or below most letters and usually written delayed in time. These delayed strokes represent a modeling challenge for the conventional left-right HMM that is commonly used for Automatic Speech Recognition (ASR) systems. In this paper, we introduce a new approach for handling delayed strokes in Arabic online handwriting recognition using HMMs. We also show that several modeling approaches such as context based tri-grapheme models, speaker adaptive training and discriminative training that are currently used in most state-of-the-art ASR systems can provide similar performance improvement for Hand Writing Recognition (HWR) systems. Finally, we show that using a multi-pass decoder that use the computationally less expensive models in the early passes can provide an Arabic large vocabulary HWR system with practical decoding time. We evaluated the performance of our proposed Arabic HWR system using two databases of small and large lexicons. For the small lexicon data set, our system achieved competing results compared to the best reported state-of-the-art Arabic HWR systems. For the large lexicon, our system achieved promising results (accuracy and time) for a vocabulary size of 64k words with the possibility of adapting the models for specific writers to get even better results.     

Arabic handwritten digit recognition

In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition. The performances of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve as a benchmark for future work on the problem. The performance of well known classifiers and feature extraction techniques will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and competes with the state-of-the-art techniques. A total of 54 different classifier/features combinations will be evaluated on Arabic digits in terms of accuracy and classification time. The results are analyzed and the problem of the digit ‘0’ is identified with a proposed method to solve it. Moreover, we propose a strategy to select and design an optimal two-stage system out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy as the highest classifier/features combination but with much less recognition time.     

A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature.     

A generic feature-independent pyramid multilevel model for Arabic handwriting recognition

Multimedia Tools and Applications - Over the last decade, several image features extraction schemes such as multi-level (ML) and pyramid multi-level (PML) have been proposed. Generally speaking,...     

Binary segmentation algorithm for English cursive handwriting recognition

Segmentation in off-line cursive handwriting recognition is a process for extracting individual characters from handwritten words. It is one of the most difficult processes in handwriting recognition because characters are very often connected, slanted and overlapped. Handwritten characters differ in size and shape as well. Hybrid segmentation techniques, especially over-segmentation and validation, are a mainstream to solve the segmentation problem in cursive off-line handwriting recognition. However, the core weakness of the segmentation techniques in the literature is that they impose high risks of chain failure during an ordered validation process. This paper presents a novel Binary Segmentation Algorithm (BSA) that reduces the risks of the chain failure problems during validation and improves the segmentation accuracy. The binary segmentation algorithm is a hybrid segmentation technique and it consists of over-segmentation and validation modules. The main difference between BSA and other techniques in the literature is that BSA adopts an un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are very promising.     

3D object recognition technique using multiple 2D views for Arabic sign language

Some objects in specific poses cannot be distinguished using a single view. A model is proposed and developed for 3D object recognition based on multiple-views; it was applied on hand postures recognition. A pulse-coupled neural network is used to generate features vector for single view. Two views with different view angles are used; each view generates its features’ vector. The two 2D-vectors are then linearly combined into one 3D vector. The hand postures are then combined to construct a dynamic gesture (word). The reconstruction is performed using best-match search algorithm. The experiment was conducted on 50 words and the result was 96% recognition accuracy confirming objects dataset offline extendibility.     

Arabic GramCheck: a grammar checker for Arabic

Arabic is a Semitic language that is rich in its morphology and syntax. The very numerous and complex grammar rules of the language may be confusing for the average user of a word processor. In this paper, we report our attempt at developing a grammar checker program for Modern Standard Arabic, called Arabic GramCheck. Arabic GramCheck can help the average user by checking his/her writing for certain common grammatical errors; it describes the problem for him/her and offers suggestions for improvement. The use of the Arabic grammatical checker can increase productivity and improve the quality of the text for anyone who writes Arabic. Arabic GramCheck has been successfully implemented using SICStus Prolog on an IBM PC. The current implementation covers a well‐formed subset of Arabic and focuses on people trying to write in a formal style. Successful tests have been performed using a set of Arabic sentences. It is concluded that the approach is promising by observing the results as compared to the output of a commercially available Arabic grammar checker. Copyright © 2005 John Wiley & Sons, Ltd.     

Printed Arabic character recognition using HMM

The Arabic Language has a very rich vocabulary. More than 200 million people speak this language as their native speaking, and over 1 billion people use it in several religion-related activities. In this paper a new technique is presented for recognizing printed Arabic characters. After a word is segmented, each character/word is entirely transformed into a feature vector. The features of printed Arabic characters include strokes and bays in various directions, endpoints, intersection points, loops, dots and zigzags. The word skeleton is decomposed into a number of links in orthographic order, and then it is transferred into a sequence of symbols using vector quantization. Single hidden Markov model has been used for recognizing the printed Arabic characters. Experimental results show that the high recognition rate depends on the number of states in each sample.     

An unconstrained handwriting recognition system

In this paper, an integrated offline recognition system for unconstrained handwriting is presented. The proposed system consists of seven main modules: skew angle estimation and correction, printed-handwritten text discrimination, line segmentation, slant removing, word segmentation, and character segmentation and recognition, stemming from the implementation of already existing algorithms as well as novel algorithms. This system has been tested on the NIST, IAM-DB, and GRUHD databases and has achieved accuracy that varies from 65.6% to 100% depending on the database and the experiment.     

Sentential Count Rules for Arabic Language

This paper presents two grammars for reading numbers of classical andmodern Arabic language. The grammars make use of the structured Arabiccounting system to present an accurate and compact grammar that can beeasily implemented in different platforms. Automating the process ofreading numbers from its numerical representation to its sentential formhas many applications. Inquiring about your bank balance over the phone,automatically writing the amount of checks (from numerical form toletter form), and reading for the blind people are some of the fieldsthat automated reading of numbers can be of service. The parsing problemof sentential representation of numbers in the Arabic language is alsoaddressed. A grammar to convert from sentential representation to thenumerical representation is also presented. Grammars presented can beused to translate from the sentential Arabic numbers to sententialEnglish numbers, and vice versa, by using the common numericalrepresentation as an intermediate code. Such methodology can be used toaid the automatic translation between the two natural languages. Allgrammars described in this paper have been implemented on a UNIX system.Examples of different number representations and the output of theimplementation of the grammars are given as part of the paper.     

A new representation of shape and its use for high performance in online Arabic character recognition by an associative memory

The purpose of this study is to investigate a new representation of shape and its use in handwritten online character recognition by a Kohonen associative memory. This representation is based on the empirical distribution of features such as tangents and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a Kohonen neural network trained using the representation. In addition to the Euclidean distance traditionally used in the Kohonen training algorithm to measure the similarities among feature vectors, we also investigate the Kullback–Leibler divergence and the Hellinger distance, functions that measure distance between distributions. Furthermore, we perform operations (pruning and filtering) on the trained memory to improve its classification potency. We report on extensive experiments using a database of online Arabic characters produced without constraints by a large number of writers. Comparative results show the pertinence of the representation and the superior performance of the scheme.