Arabic Handwritten Word Recognition Research Articles

Deep Learning for Accurate Recognition of Arabic Handwritten Words in Historical Documents.

Invaluable for historical research, Arabic manuscripts are often challenging to access due to their physical preservation needs. Digitization coupled with handwriting recognition offers a powerful solution for making these resources readily available. However, accurate text recognition hinges on effective segmentation into lines and words. While line segmentation has received significant attention, word detection in historical Arabic script remains an under-explored area due to the scarcity of annotated datasets. This paper addresses this gap by introducing a novel, word-level annotated database specifically designed for historical Arabic manuscripts. We further present two neural network architectures based on Transformers relying on an Arabic language model and a robust CNN-BLSTM with skip connections, guaranteeing the preservation of crucial spatial information for recognition. Validation on a dataset of 20 pages of historical manuscripts demonstrates the effectiveness of the proposed models, achieving a Character Error Rate (CER) of 4.8% with data augmentation, surpassing the state-of-the-art.

Cursive Arabic handwritten word recognition system using majority voting and k-NN for feature descriptor selection

Cursive Arabic handwritten word recognition system using majority voting and k-NN for feature descriptor selection

Arabic Handwritten Word Recognition System Based on the Wavelet Packet Decomposition

This paper attempts to recognize Arabic handwriting based on the Wavelet Packet Decomposition (WPD) using two different classifiers (Support Vector Machine SVM with three kernels and k-Nearest Neighbors K-NN). The proposed approach of recognizing Arabic handwriting contains three major stages including image preprocessing, extracting the features of the image, and classification. Firstly, the diacritics are removed using the opening morphological operation (i.e image preprocessing). Secondly, extracting the structure of the paragraph using the morphological method. Finally, the word image size is converted into a suitable size for the next stages. To extract features from the image, the WPD method was adopted to extract the features of Arabic handwriting as the transformation method of feature space. This extracts the Arabic global features to be classified in the last stage using the SVM with polynomial kernel and K-NN. The proposed approach of recognizing Arabic handwriting was tested on IFN/ENIT dataset by rescaling images into various sizes, 93.7% when the SVM with polynomial kernel is used, K-NN classifier achieved accuracy rate is 88.4%.

Efficient feature descriptor selection for improved Arabic handwritten words recognition

<span lang="EN-US">Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the </span><em><span lang="EN-US">Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis</span></em><span lang="EN-US"> (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.</span>

Handwritten Arabic and Roman word recognition using holistic approach

Handwritten Arabic and Roman word recognition using holistic approach

Recognition of Arabic handwritten words using convolutional neural network

<span>A new method for recognizing automatically Arabic handwritten words was presented using convolutional neural network architecture. The proposed method is based on global approaches, which consists of recognizing all the words without segmenting into the characters in order to recognize them separately. Convolutional neural network (CNN) is a particular supervised type of neural network based on multilayer principle; our method needs a big dataset of word images to obtain the best result. To optimize our system, a new database was collected from the benchmarking Arabic handwriting database using the pre-processing such as rotation transformation, which is applied on the images of the database to create new images with different features. The convolutional neural network applied on our database that contains 40320 of Arabic handwritten words (26880 images for training set and 13440 for test set). Thus, different configurations on a public benchmark database were evaluated and compared with previous methods. Consequently, it is demonstrated a recognition rate with a success of 96.76%.</span>

Arabic Handwritten Word Recognition Based on Stationary Wavelet Transform Technique using Machine Learning

This paper is aimed at improving the performance of the word recognition system (WRS) of handwritten Arabic text by extracting features in the frequency domain using the Stationary Wavelet Transform (SWT) method using machine learning, which is a wavelet transform approach created to compensate for the absence of translation invariance in the Discrete Wavelets Transform (DWT) method. The proposed SWT-WRS of Arabic handwritten text consists of three main processes: word normalization, feature extraction based on SWT, and recognition. The proposed SWT-WRS based on the SWT method is evaluated on the IFN/ENIT database applying the Gaussian, linear, and polynomial support vector machine, the k-nearest neighbors, and ANN classifiers. ANN performance was assessed by applying the Bayesian Regularization (BR) and Levenberg-Marquardt (LM) training methods. Numerous wavelet transform (WT) families are applied, and the results prove that level 19 of the Daubechies family is the best WT family for the proposed SWT-WRS. The results also confirm the effectiveness of the proposed SWT-WRS in improving the performance of handwritten Arabic word recognition using machine learning. Therefore, the suggested SWT-WRS overcomes the lack of translation invariance in the DWT method by eliminating the up-and-down samplers from the proposed machine learning method.

Offline Arabic handwritten word recognition: A transfer learning approach

Offline Arabic handwritten word recognition is still a challenging task. Many deep learning approaches perform admirably on this task if the lexicon size is not too large and the number of training samples is sufficient for the training process. The transfer learning technique is commonly used to compensate for the lack of training samples, but there is a wide controversy about the effectiveness of applying it to cross-domain tasks. In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. Then, we evaluate the performance of the ResNet18 model that has been pre-trained on the ImageNet dataset for the same task. Finally, we propose an approach based on sequentially transferring the mid-level word image representations through two consecutive phases using the ResNet18 model. We carried out four different sets of experiments using two popular offline Arabic handwritten word datasets: the AlexU-W and the IFN/ENIT (v2.0p1e) to figure out the most effective way of applying transfer learning. Our results demonstrate that using the ImageNet as a source dataset improves the recognition accuracy of the ten frequently misclassified words in the IFN/ENIT dataset by 14%, while our proposed approach gives a rise of 35.45%. In the whole dataset, we achieved recognition accuracy up to 96.11%, which is nearly a 2.5% enhancement compared with other state-of-the-art methods.

Isolated Arabic handwritten words recognition using EHD and HOG methods

<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

Automatic recognition of handwritten Arabic using maximally stable extremal region features

Arabic script is inherently cursive in nature; therefore, optical character recognition (OCR) techniques developed for other scripts are generally inappropriate for Arabic. We present an OCR system for the recognition of handwritten Arabic words. Several original contributions have been made. First, we have proposed a novel feature vector to represent handwritten Arabic words based on the ellipsoid approximation of maximally stable extremal regions (MSERs). This feature vector is compact, robust, and well suited to handle writer-induced variation in handwritten script. Second, we present a new database, Saudi Arabian city name (SACN) database consisting of handwritten Saudi Arabian city names, which is the first such publicly available database. Finally, we present promising experimental results on two databases. One of the main experiments compares the proposed MSER feature vector with several well-established feature extraction methods using the proposed SACN database. The proposed feature vector performed better than all the standardized methods, with 100% correct identification rate. Another experiment is designed to compare our system to other recent studies on handwritten Arabic text recognition using the extensively employed IFN/ENIT database. Using the standard experimental protocol, our system outperformed all others with 92.64% correct identification rate.

A Deep Learning Approach for Handwritten Arabic Names Recognition

Optical Character recognition (OCR) has enabled many applications as it has attained high accuracy for all printing documents and also for handwriting of many languages. How-ever, the state-of-the-art accuracy of Arabic handwritten word recognition is far behind. Arabic script is cursive (both printed and handwritten). Therefore, traditionally Arabic recognition systems segment a word to characters first before recognizing its characters. Arabic word segmentation is very difficult because Arabic letters contain many dots. Moreover, Arabic letters are context sensitive and some letters overlapped vertically. A holis-tic recognizer that recognizes common words directly (without segmentation) seems the plausible model for recognizing Arabic common words. This paper presents the result of training a Conventional Neural Network (CNN), holistically, to recognize Arabic names. Experiments result shows that the proposed CNN is distinct and significantly superior to other recognizers that were used with the same dataset.

Hybrid one‑class classifier ensemble based on fuzzy integral for open‑lexicon handwritten Arabic word recognition

Hybrid one‑class classifier ensemble based on fuzzy integral for open‑lexicon handwritten Arabic word recognition

Hybrid one-class classifier ensemble based on fuzzy integral for open-lexicon handwritten Arabic word recognition

One-class classifier (OCC) is involved for solving different kinds of problems due to its ability to represent a class distribution regardless the remaining classes. Its main advantage for multi-class classification is offering an open system and therefore allows easily extending new classes without retraining OCCs. So far, hidden Markov models, support vector machines and neural networks are the most used classifiers for Arabic word recognition, which provides a system with closed lexicon. In this paper, the OCCs are explored in order to perform an Arabic word recognition system with an open lexicon. Generally, pattern recognition systems designed by a single system suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining multiple systems becomes an attractive research topic for performance and robustness enhancement. Fixed rules are commonly used us combiners for the hybrid OCC ensembles. The present paper aims to propose a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Furthermore, an alternative framework is proposed to design a parameter-independent and open-lexicon handwritten Arabic word recognition system as well as a new density measure function. Experimental results conducted on Arabic handwritten dataset using different types of OCCs with large number of classes highlight the superiority of FI for hybrid OCC ensembles.

Recognition of Arabic Handwritten Words using Gabor-based Bag-of-Features Framework

Recognition of Arabic Handwritten Words using Gabor-based Bag-of-Features Framework

An efficient multiple classifier system for Arabic handwritten words recognition

In this paper, we propose an efficient multiple classifier system for Arabic handwritten words recognition. First, we use Chebyshev moments (CM) enhanced with some Statistical and Contour-based Features (SCF) for describing word images. Then, we combine several classifiers integrated at the decision level. We consider the multilayer perceptron (MLP), the support vector machine (SVM) and the Extreme Learning Machine (ELM) classifiers. We propose several combination rules between MLP, SVM and ELM classifiers trained with CM and SCF features. Further, we consider a second level of combination that merges three best rules among the proposed ones. The system is evaluated on the IFN/ENIT database and compared to some well-known systems for Arabic handwriting recognition. The numerical results are competitive and show that our system is able to achieve a global recognition rate equal to 96.82% for the considered dataset.

Pixel distribution-based features for offline Arabic handwritten word recognition

Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing. The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic script presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model PDM for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers.

Pixel distribution-based features for offline Arabic handwritten word recognition

Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing. The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic script presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model (PDM) for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers.

Offline Arabic Handwriting Recognition System Based on the Combination of Multiple Semi-Continuous HMMs

In this paper, we present a multi-classifier approach for off-line handwritten Arabic word recognition system. The main objective of this paper is to develop a handwriting recognition system that can be learned and applied to different Arabic writing styles. We propose an approach that combines multiple classifiers based on semi-continuous Hidden Markov Model using different feature extraction methods. The following process consists of several phases: pre-processing, extraction of pertinent characteristics and modeling. The role of the pre-processing phase is to prepare the input image text, i.e, binarization, normalization, segmentation and skeletonization. The obtained images are then used to extract features using a mixture of geometrical and statistical characteristics, namely, the intensities of gray level of the pixel, the densities and the concavities of the pixels, the VH2D projections and the invariants Hu moments. The modeling phase is based on the Hidden Markov Model using the HTK tools for the training and the recognition phase. Each character is modeled by a semi-continuous HMMs, and each set of feature has its own HMM. To improve the performance of our Arabic handwriting recognition system, we propose to combine parallel methods of HMMs having the same architecture, but training phase using four different types of primitive vectors. Our system was evaluated using the ENIT/IFN the base Arabic data. Results Obtained show that the combination of four semi-continuous HMM classifiers gives a significant improvement of our off-line handwriting recognition system compared to results obtained when using individual classifiers.

A PGM-based System for Arabic HandwrittenWord Recognition

This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple andeasily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders and diacritic points. The system is evolved based on horizontal and vertical Hidden Markov Models and Dynamic Bayesian Network. Our strategy consists of looking for various architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT database and ancient manuscripts strongly support the feasibility of the proposed system. The recognition rates achieve 91.89% (IFN/ENIT) and 94.61% (ancient manuscripts).

A Framework for Arabic Handwritten Recognition Based on Segmentation

Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In the training phase, Arabic handwritten characters were trained to be recognized, while in the testing phase, words were segmented into characters for recognition. Classification is achieved in two steps (classification of the segmented characters and classification of the word). A dictionary is constructed and used to correct any errors occurring during the previous stages of the recognition process. This work has been tested with IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.