Handwritten Arabic Character Recognition Research Articles

A Survey on Arabic Handwritten Script Recognition Systems

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted

Recognition of Arabic Handwritten Diacritics using the new database DBAHD

Recognition of handwritten Arabic characters is gaining momentum and research in this area has increased considerably in recent years. However, research remains modest compared to that performed in other scripts. This is mainly due to the morphology of Arabic writing, in particular its richness in diacritical marks. This signs are generally recognized by adopting structural or morphological measures. However, the difficulty and variability of handwriting can sometimes be misleading, thus influencing the results obtained. This article presents a new database for Arabic handwritten diacritics (DBAHD). It is designed to serve the Arabic handwriting recognition systems based on segmentation and machine learning.

A new Arabic handwritten character recognition deep learning system (AHCR-DLS)

Optical character recognition for the English text may be considered one of the most important research topics, whether, printed or handwritten. Although excellent results have been reached in the English text, there is a lack of this type of research in the Arabic text. This is because of the nature of the Arabic alphabet, and the multiplicity of forms of the same letter. Arabic handwritten character recognition (AHCR) systems involve several issues, and challenges from finding a suitable, and public Arabic handwritten text dataset phase to recognition, and classification phase passing through segmentation, and feature extraction phases. The paper objectives are: Firstly, a large, and complex Arabic handwritten characters’ dataset (HMBD) is presented for training, testing, and validation phases, as well as, discussing its collection, preparation, cleaning, and preprocessing. Secondly, we introduce a deep learning (DL) system with two convolutional neural network (CNN) architectures (named HMB1 and HMB2); with the appliance of optimization, regularization, and dropout techniques. This system can serve as a baseline for future research on handwritten Arabic text. Different performance metrics were calculated such as accuracy, recall, precision, and F1. 16 experiments were applied to the described system using HMBD, and another two datasets: CMATER, and AIA9k. Experiments’ results were captured and compared to study the effects of weight initializers, optimizers, data augmentation, and regularization on overfitting, and accuracy. He Uniform weight initializer and AdaDelta optimizer reported the highest accuracies. Data augmentation showed an improvement in the accuracies. HMB1 reported testing accuracy of 98.4% with 865,840 records using augmentation on HMBD. CMATER and AIA9k datasets were used for validating the generalization. Data augmentation was applied, and the best results were 100%, and 99.0% for testing accuracies, respectively. A cross-over validation between the described architectures, and a previous state-of-the-art architecture, and dataset was performed in two phases. First, the previous control architecture cannot generalize for the presented dataset in the current study. Second, the study described architectures generalize for the control dataset, with higher accuracies (97.3%, and 96.8% for HMB1, and HMB2, respectively), than the reported accuracy in the selected control study.

Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches

Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches

Automatic recognition of handwritten Arabic characters: a comprehensive review

The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field.

A Survey on Arabic Handwritten Character Recognition

There are much heavy studies on handwritten character recognition (HCR) for nearly previous four decades. The research on some of the common script like Arabic, Indian and Chinese has been done. This manuscript presents a survey of character recognition on Arabic script, and most of the popular published paper methods are summarized and also analyzed different methods for building a robust system of HCR and included some future research on recognition direction of handwritten character. The paper analyzed and presented various algorithms with respect to preprocessing methods, segmentation methods, feature extraction methods and various classification approaches of the Arabic character recognition.

Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified Abstract Syntax Tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.

Handwritten Arabic Optical Character Recognition Approach Based on Hybrid Whale Optimization Algorithm With Neighborhood Rough Set

Accomplishing high recognition performance is considered one of the most important tasks for handwritten Arabic character recognition systems. In general, Optical Character Recognition (OCR) systems are constructed from four phases: pre-processing, feature extraction, feature selection, and classification. Recent literature focused on the selection of appropriate features as a key point towards building a successful and sufficient character recognition system. In this paper, we propose a hybrid machine learning approach that utilizes neighborhood rough sets with a binary whale optimization algorithm to select the most appropriate features for the recognition of handwritten Arabic characters. To validate the proposed approach, we used the CENPARMI dataset, which is a well-known dataset for machine learning experiments involving handwritten Arabic characters. The results show clear advantages of the proposed approach in terms of recognition accuracy, memory footprint, and processor time than those without the features of the proposed method. When comparing the results of the proposed method with other recent state-of-the-art optimization algorithms, the proposed approach outperformed all others in all experiments. Moreover, the proposed approach shows the highest recognition rate with the smallest consumption time compared to deep neural networks such as VGGnet, Resnet, Nasnet, Mobilenet, Inception, and Xception. The proposed approach was also compared with recently published works using the same dataset, which further confirmed the outstanding classification accuracy and time consumption of this approach. The misclassified failure cases were studied and analyzed, which showed that they would likely be confusing for even Arabic natives because the correct interpretation of the characters required the context of their appearance.

Handwritten Arabic Characters Recognition using a Hybrid Two-Stage Classifier

Handwritten Arabic character recognition presents a big challenge to researchers in the field of pattern recognition. Arabic characters are characterized by their highly-cursive nature and many of them have a similar appearance. For example, the only difference between some of the alphabet characters is the existence of a number dots above or below the main character shape. This paper proposes a system for isolated off-line handwritten Arabic character recognition using the Discrete Cosine Transform (DCT) as the feature extraction method and a two-stage hybrid classifier. The two stages are a Support Vector Machine (SVM) and a neural network (NN). The first stage is a two-class SVM classifier which classifies a character either a character with dot(s) or without dot(s). The output of this stage is used to extend the feature vector of the character by the class value to give it an extra unique feature. The extend feature vector is fed to a multi-class neural network model to classify the character. The proposed approach is tested on a database of Arabic handwritten characters called AlexU Isolated Alphabet (AIA9K) containing 8,737 character images. The experimental results of the first stage classifier showed a high recognition accuracy rate of 99.14%. The proposed two-stage hybrid classifier obtained an average recognition accuracy rate of 91.84% over all Arabic Alphabet characters.

Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition

Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition

Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine

Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM. Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters. Moreover, we determine the error classification rate (ECR). The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters. Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters. The comparative evaluation is stated and the system accuracy reached 95.07% CRR with 4.93% ECR compared with the state of the art.

Arabic Hand Written Character Recognition Based on Contour Matching and Neural Network

Complexity of Arabic writing language makes its handwritten recognition very complex in terms of computer algorithms. The Arabic handwritten recognition has high importance in modern applications. The contour analysis of word image can extract special contour features that discriminate one character from another by the mean of vector features. This paper implements a set of pre-processing functions over a handwritten Arabic characters, with contour analysis, to enter the contour vector to neural network to recognize it. The selection of this set of pre-processing algorithms was completed after hundreds of tests and validation. The feed forward neural network architecture was trained using many patterns regardless of the Arabic font style building a rigid recognition model. Because of the shortcomings in Arabic written databases or datasets, the testing was done by non-standard data set. The presented algorithm structure got recognition ratio about 97%.

Convolutional Neural Network Model for Arabic Handwritten Characters Recognition

Convolutional Neural Network Model for Arabic Handwritten Characters Recognition

Convolutional Neural Network Model for Arabic Handwritten Characters Recognition

Convolutional Neural Network Model for Arabic Handwritten Characters Recognition

A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

Convolutional ensembles for Arabic Handwritten Character and Digit Recognition.

A learning algorithm is proposed for the task of Arabic Handwritten Character and Digit recognition. The architecture consists on an ensemble of different Convolutional Neural Networks. The proposed training algorithm uses a combination of adaptive gradient descent on the first epochs and regular stochastic gradient descent in the last epochs, to facilitate convergence. Different validation strategies are tested, namely Monte Carlo Cross-Validation and K-fold Cross Validation. Hyper-parameter tuning was done by using the MADbase digits dataset. State of the art validation and testing classification accuracies were achieved, with average values of 99.74% and 99.47% respectively. The same algorithm was then trained and tested with the AHCD character dataset, also yielding state of the art validation and testing classification accuracies: 98.60% and 98.42% respectively.

Spatial and Textural Aspects for Arabic Handwritten Characters Recognition

Spatial and Textural Aspects for Arabic Handwritten Characters Recognition

Spatial and Textural Aspects for Arabic Handwritten Characters Recognition

The purpose of the present paper is the recognition of handwritten Arabic characters in their isolated form. The specificity of Arabic characters is taken into consideration, each of the proposed feature extraction method integrates one of the two aspects: spatial and textural. In the first step, a modified Bitmap Sampling method is proposed, which converts the character’s images into a binary Matrix and then constructs a Mask for each class. A matching rate is used between the input binary matrix and the masks to determinate the corresponding class. In the second step we investigate the use of an Artificial Neural Network as classifier with the binary matrices as features and then the histograms of Local Binary Patterns to capture the texture aspect of the characters. Finally, the results of these two methods are combined to take into consideration both aspects at the same time. Tested on the Arabic set of the Isolated Farsi Handwritten Character Database, the proposed method has 2.82% error rate

Hybrid Feature Vector for the Recognition of Arabic Handwritten Characters Using Feed-Forward Neural Network

Character recognition is one of the most successful applications of artificial neural network methods. However, most of the work has been dedicated to the recognition of Latin handwritten characters, and only few studies have been devoted to the recognition of Arabic handwritten characters. This paper presents an off-line recognition system for Arabic handwritten characters. In this context, we use 66 statistical, structural, and regional characteristics extracted using five methods. These characteristics are treated by a feed-forward neural network with a hidden layer. To this end, we use our database for Arabic handwritten characters and ligatures (DBAHCL) in the learning, test and validation phases. The accuracy generated by our system is about 98.27%.

Investigation on deep learning for off-line handwritten Arabic character recognition

Handwritten character recognition is a system widely used in the modern world and it is still an important challenge. Traditional machine-learning techniques require careful engineering and considerable domain expertise to transform raw data into a feature vector from which the classifier could classify the input pattern. To cope with this problem, the popular Deep Convolutional Neural Networks (DCNN), introduced recently, have effectively replaced the hand-crafted descriptors with network features and have been shown to provide significantly better results than traditional methods. It is one of the fastest growing areas in machine learning, promising to reshape the future of artificial intelligence. However, the problem with deep learning is that it requires large datasets for training because of the huge number of parameters needed to be tuned by a learning algorithm. CNN model can be used in three different ways: (i) training the CNN from scratch; (ii) using the transfer learning strategy to leverage features from a pre-trained model on a larger dataset; and (iii) keeping the transfer learning strategy and fine-tune the weights of CNN architecture. In this work, we investigate the applicability of DCNN using transfer learning strategies on two datasets; a new expanded version of our recently proposed database for off-line isolated handwritten Arabic character, referred to as OIHACDB and AHCD. Our results showed satisfactory recognition accuracies and outperform all other prominent exiting methods in the field of Handwritten Arabic Character Recognition (HACR).