Arabic Word Recognition Research Articles

A Multi-Stream Hmm Approach to Offline Handwritten Arabic Word Recognition

In This paper we presented new approach for cursive Arabic text recogniti on system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phaseis the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by twomethods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diag onals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi -stream approach.

Arabic Word Recognition Based on 3D Radon and Multiwavelet Neural Network

Arabic Word Recognition Based on 3D Radon and Multiwavelet Neural Network

Offline arabic handwritten text recognition

Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features and classifications techniques utilized in recent research work have diversified noticeably compared to the past. Moreover, more efforts have been diverted, in last few years, to construct different databases for Arabic handwriting recognition. This article provides a comprehensive survey of recent developments in Arabic handwriting recognition. The article starts with a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system. Then the used databases for Arabic text recognition are discussed. Research works on preprocessing phase, like text representation, baseline detection, line, word, character, and subcharacter segmentation algorithms, are presented. Different feature extraction techniques used in Arabic handwriting recognition are identified and discussed. Different classification approaches, like HMM, ANN, SVM, k-NN, syntactical methods, etc., are discussed in the context of Arabic handwriting recognition. Works on Arabic lexicon construction and spell checking are presented in the postprocessing phase. Several summary tables of published research work are provided for used Arabic text databases and reported results on Arabic character, word, numerals, and text recognition. These tables summarize the features, classifiers, data, and reported recognition accuracy for each technique. Finally, we discuss some future research directions in Arabic handwriting recognition.

From static to dynamic ensemble of classifiers selection: Application to Arabic handwritten recognition

Arabic handwriting word recognition is a challenging problem due to Arabic's connected letter forms, consonantal diacritics and rich morphology. One way to improve the recognition rates classification task is to improve the accuracy of individual cla

Freeman Chain Code Contour Processing for Handwritten Isolated Arabic Characters Recognition

The Arabic characters are being used in various languages. Recognition of handwritten characters of Arabic alphabet set is an important area of research. The work on recognition of Handwritten Isolated Arabic Characters (HIAC) is still an open research problem and it has numerous applications. Machine reading of optically scanned text is usually called optical Character recognition (OCR). In this paper we present an OCR for Handwritten Isolated Arabic Characters. Basic Characters are recognized by Freeman chain code. The proposed method obtains the contour of the character and then generates a Freeman Chain Code for contoured character. The obtained Freeman chain code is unique for handwritten isolated characters. The present method is based on template matching to recognize handwritten printed isolated Arabic characters. It was trained and validated on 200 images (consisting of 7400 Arabic characters written by 18 writers). Our experimentation observed the overall recognition rate is 95%. It is carried out in order to improve the rate and the performance of an Arabic handwritten word recognition system. The proposed system has been implemented and tested on Matlab R2008b environment.

SEGMENTATION-FREE ONLINE ARABIC HANDWRITING RECOGNITION

Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation. In the second stage, features are extracted from each of the normalized words, where a set of new features for handwritten Arabic words is proposed, based on a sliding window approach moving across the mirrored word image. The third stage is for classification and recognition, where machine learning is applied using HMMs and DBNs. In order to validate the techniques, extensive experiments were conducted using the IFN/ENIT database which contains 32,492 Arabic words. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity.

Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

This paper describes a new approach to combine a multilayer perceptron (MLP) and a hidden Markov model for recognizing handwritten Arabic words. As a first step, connected components (CCs) of black pixels are detected, then the system determines which CCs are sub-words and which are diacritics. The diacritics are then isolated and identified separately, and the sub-words are segmented into graphemes. The MLP is used as labeller (classifier) and probability estimator. We also introduce the diacritics and their positions in our hybrid system; thus, only one model including both grapheme and diacritic states is built to represent the whole alphabet. Finally, we consider a maximum likelihood classifier to decide about the word class. The experiments that were performed show promising results on Arabic word segmentation and recognition.

Natural Language Morphology Integration in Off-Line Arabic Optical Text Recognition

In this paper, we propose a new linguistic-based approach called the affixal approach for Arabic word and text image recognition. Most of the existing works in the field integrate the knowledge of the Arabic language in the recognition process in two ways: either in post-recognition using the language of dictionary (dictionary of words) to validate the word hypotheses suggested by the OCR or in the course of the recognition process (recognition directed by a lexicon) using a statistical model of the language (Hidden Markov Model or N-gram). The proposed approach uses the linguistic concepts of the vocabulary to direct and simplify the recognition process. The principal contribution of the proposed approach is to be able to categorize the word hypotheses in words that are either derived or not derived from roots and to characterize morphologically each word hypothesis in order to prepare the text hypotheses for later analyses (for example, syntactic analysis; to filter the sentence hypotheses).

An efficient speech recognition system in adverse conditions using the nonparametric regression

General Regression Neural Networks (GRNN) have been applied to phoneme identification and isolated word recognition in clean speech. In this paper, the authors extended this approach to Arabic spoken word recognition in adverse conditions. In fact, noise robustness is one of the most challenging problems in Automatic Speech Recognition (ASR) and most of the existing recognition methods, which have shown to be highly efficient under noise-free conditions, fail drastically in noisy environments. The proposed system was tested for Arabic digit recognition at different Signal-to-Noise Ratio (SNR) levels and under four noisy conditions: multispeakers babble background, car production hall (factory), military vehicle (leopard tank) and fighter jet cockpit (buccaneer) issued from NOISEX-92 database. The proposed scheme was successfully compared to the similar recognizers based on the Multilayer Perceptrons (MLP), the Elman Recurrent Neural Network (RNN) and the discrete Hidden Markov Model (HMM). The experimental results showed that the use of nonparametric regression with an appropriate smoothing factor (spread) improved the generalization power of the neural network and the global performance of the speech recognizer in noisy environments.

Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

In this paper, we propose and describe efficient multiclass classification and recognition of unconstrained handwritten Arabic words using machine learning approaches which include the K-nearest neighbor (K-NN) clustering, and the neural network (NN). The technical details are presented in terms of three stages, namely preprocessing, feature extraction and classification. Firstly, words are segmented from input scripts and also normalized in size. Secondly, from each of the segmented words various feature extraction methods are introduced. Finally, these features are utilized to train the K-NN and the NN classifiers for classification. In order to validate the proposed techniques, extensive experiments are conducted using the K-NN and the NN. The proposed algorithms are tested on the IFN/ENIT database which contains 32492 Arabic words; the proposed algorithms give good accuracy when compared with other methods.

Improvement of the speech recognition in noisy environments using a nonparametric regression

In this paper, an efficient speech recognition system based on the general regression neural network (GRNN) has been presented. The GRNN has been previously applied for phoneme identification and isolated word recognition in quiet environment. We propose to extend this method to Arabic spoken word recognition in adverse conditions because noise robustness is one of the most challenging problems in automatic speech recognition (ASR). The proposed system has been tested for Arabic digit recognition at different signal-to-noise ratio (SNR) levels in various noisy conditions, including stationary and nonstationary background noises issued from NOISEX-92 database. The proposed scheme is compared with the similar recognisers based on the multilayer perceptron (MLP), the Elman recurrent neural network (RNN) and the discrete hidden Markov model (HMM). The experimental results show that the use of the neural network approach including nonparametric regression improves the global performance of the speech recogniser in noisy environments.

TWO-STAGE LEXICON REDUCTION FOR OFFLINE ARABIC HANDWRITTEN WORD RECOGNITION

Given large number of words to be recognized, a two-stage strategy for eliminating unlikely candidates before recognition can be a reasonable and powerful approach for increasing the recognition speed. A holistic lexicon reduction technique for offline handwritten Arabic word recognition is proposed in this paper. The principle of this technique involves the extraction of dots and subwords from the cursive Arabic word image to describe its shape. In the first stage of reduction, the number of subwords in the input word is estimated. Then in the second stage, the word descriptor, based on the dots information, is used while taking into account only the candidates selected in the first stage. Experimental results on IFN/ENIT database, consisting of 26,459 cursive Arabic word images, show a lexicon reduction of 92.5% with accuracy of 74%.

Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

In this paper, we describe an off-line unconstrained handwritten Arabic word recognition system based on segmentation-free approach and semi-continuous hidden Markov models (SCHMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact that HMMs are deficient in modeling character durations properly. We will show experimentally that explicit state duration modeling in the SCHMM framework can significantly improve the discriminating capacity of the SCHMMs to deal with very difficult pattern recognition tasks such as unconstrained handwritten Arabic recognition. In order to carry out the letter and word model training and recognition more efficiently, we propose a new version of the Viterbi algorithm taking into account explicit state duration modeling. Three distributions (Gamma, Gauss and Poisson) for the explicit state duration modeling have been used and a comparison between them has been reported. To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image. Several experiments have been performed using the IFN/ENIT benchmark database and the best recognition performances achieved by our system outperform those reported recently on the same database.

Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration

We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact that HMMs are deficient in modeling character durations properly. We will show experimentally that explicit state duration modeling in the HMM framework can significantly improve the discriminating capacity of the HMMs to deal with very difficult pattern recognition tasks such as unconstrained Arabic handwriting recognition. In order to carry out the letter and word model training and recognition more efficiently, we propose a new version of the Viterbi algorithm taking into account explicit state duration modeling. Three distributions (Gamma, Gauss, and Poisson) for the explicit state duration modeling have been used, and a comparison between them has been reported. To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image. Several experiments have been performed using the IFN/ENIT benchmark database and the best recognition performances achieved by our system outperform those reported recently on the same database.

HMM-BASED SYSTEM FOR RECOGNIZING WORDS IN HISTORICAL ARABIC MANUSCRIPT

This paper presents an omni-font Arabic word recognition system. The system is based on multiple Hidden Markov Models (HMMs). Each word in the lexicon is represented with a distinct HMM. The proposed system first extracts a set of spectral features from word images, then uses those features to tune HMM parameters. The performance of the proposed system is assessed using a corpus that includes both handwritten and computer-generated scripts. The likelihood probability of the input pattern is calculated against each word model and the pattern is assigned to the model with the highest probability.

Toward a generalization of neuro-symbolic recognition: An application to arabic words

In this article, we suggest an automated construction of knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. First, ideal samples of the considered lexicon words are submitt

A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition

In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm uses this knowledge to build a multilayer neural network. It determines precisely its architecture and initializes its connections with specific values rather than random values, as is the case in classical neural networks. This construction approach provides the network with theoretical knowledge and reduces the training stage, which remains necessary because of styles and writing conditions variability. After this empirical training stage using real examples, the network acquires a final topology, which allows it to recognize new handwritten words. The proposed method has been tested on the automated construction of neuro-symbolic classifiers for two Arabic lexicons: literal amounts and city names. The application of this approach to the recognition of handwritten words or characters in different scripts and languages is also considered.

Arabic Words Recognition by Fuzzy Classifier

Arabic Words Recognition by Fuzzy Classifier

Arabic Word Recognition by Classifiers and Context

Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the "best". In recent years, the combination of different classifiers and the use of contextual information .have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme. The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.