Handwritten Arabic Character Recognition Research Articles

Automatic arabic handwritten characters Recognition using ensemble of convolutional neural networks from scratch

Automatic arabic handwritten characters Recognition using ensemble of convolutional neural networks from scratch

Enhancing the Recognition of Handwritten Arabic Characters through Hybrid Convolutional and Bidirectional Recurrent Neural Network Models

Handwritten Arabic character recognition remains a challenging task in pattern recognition due to the inherent complexities of the cursive script and visual similarities between characters. While deep learning techniques have demonstrated promising results in this domain, further enhancements to the model architecture can drive even greater performance improvements. This study introduces a hybrid deep learning approach that combines Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). By leveraging the strengths of both convolutional and recurrent neural network components, the proposed models are able to effectively capture spatial features as well as model the temporal dynamics and contextual relationships present in handwritten Arabic text. Experiments conducted on the AHCD and Hijjaa benchmark datasets show that the CNN-Bi-GRU framework achieved state-of-the-art accuracy rates of 97.05% and 91.78% respectively, outperforming previous deep learning-based methods. These results demonstrate the significant performance gains that can be achieved by integrating specialized temporal modeling and contextual representation capabilities into the handwriting recognition pipeline, without the need for explicit segmentation. The findings of this research represent a crucial advancement in the continued development of sophisticated and precise deep learning systems for Arabic handwriting recognition, with broad applications across domains that rely on efficient text extraction from handwritten documents.

The Impact of Various Factors on the Convolutional Neural Networks Model on Arabic Handwritten Character Recognition

The Impact of Various Factors on the Convolutional Neural Networks Model on Arabic Handwritten Character Recognition

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Toward Innovative Recognition of Handwritten Arabic Characters: A Hybrid Approach with SIFT, BoVW, and SVM classification

The goal of handwriting recognition has been a top priority for those who want to enter data into computer systems for more than thirty years. In several fields, the advent of handwriting recognition technology is highly anticipated. OCR technology has made it possible for computers to recognize characters as visual objects and collect data about their unique characteristics in recent years. In particular, several studies in this field have focused on Arabic writing. The use of machines to examine handwritten papers is the first step in the character identification process. The identification of specific Arabic characters is the main goal of this particular investigation. In computer vision, Arabic character recognition is very important since it's necessary to correctly recognize and classify Arabic letters and characters in manuscripts. In this research, an innovative approach based on identifying Arabic character characteristics using BoVW (bag of visual words) and SIFT (Scale Invariant Feature Transform) features is proposed. These features are clustered using k-means clustering to produce a dictionary. Following that, SVM (Support Vector Machine) is utilized to classify the word images in a visual codebook created using these terms. The proposed approach is an innovative method to deal with the difficulties associated with Arabic hand-writing recognition. The utilization of BoVW and SIFT features is expected to enhance the system's robustness in recognizing and classifying Arabic characters. The proposed approach will be experimentally evaluated using a dataset that includes a variety of Arabic characters written in various styles. The results of this study will offer important new perspectives on the effectiveness and practicality of the approach suggested

HACR-MDL: HANDWRITTEN ARABIC CHARACTER RECOGNITION MODEL USING DEEP LEARNING

Abstract. Despite the enormous effort and prior research, Arabic handwritten character recognition still has a deep, wide-ranging, and untapped scope for study owing to the enormous challenges faced in this research area. The reason for such challenges is that the Arabic script comprises 28 alphabets, each of which can be written in two to four different forms depending on where it appears in a word—beginning, middle, end, or isolated. The Convolutional Neural Network (CNN or ConvNet) is a subtype of neural network that is commonly used in image classification, speech recognition, video processing, object detection, and segmentation because its built-in convolutional layer reduces the high dimensionality of images without losing significant information. Hence, the scope of this study is to examine the classification performance of various deep CNN models on offline handwritten Arabic character recognition. Based on the experimental comparative studies, this research proposes a Handwritten Arabic Character Recognition Model using Deep Learning (HACR-MDL), a modified CNN model. The proposed model is trained and tested using the AHCD dataset achieving an accuracy of 98.54%. The results achieved showed that HACR outperformed the recent research offline handwritten Arabic character recognition in terms of model complexity, speed, model parameters, and performance metrics.

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination.

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults' handwriting, children's handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children's handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children's handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.

Recent advances of ML and DL approaches for Arabic handwriting recognition: A review

Handwritten text recognition remains a popular area of research. An analysis of these techniques is more necessary. This article is practically interested in a bibliographic study on existing recognition systems with the aim of motivating researchers to look into these techniques and try to develop more advanced ones. It presents a detailed comparative study carried out on some Arabic handwritten character recognition techniques using holistic, analytical and a segmentation-free approaches. In this study, first, we show the difference between different recognition approaches: deep learning vs machine learning. Secondly, a description of the Arabic handwriting recognition process regrouping pre-processing, feature extraction and segmentation was presented. Then, we illustrate the main techniques used in the field of handwriting recognition and we make a synthesis of these methods.

Handwritten Arabic Character Recognition for Children Writing Using Convolutional Neural Network and Stroke Identification

Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with the variability of patterns caused by factors such as the writer’s age. Most studies have focused on adults, with only one recent study on children. Moreover, many recent machine-learning methods have focused on using Convolutional Neural Networks (CNNs), a powerful class of neural networks that can extract complex features from images. In this paper, we propose a convolutional neural network (CNN) model that recognizes children’s handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of Arabic characters written by children, and 97% on the Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it revealed a bigger challenge to solve for children’s Arabic handwritten character recognition. Moreover, we proposed a new approach using multiple models instead of a single model based on the number of strokes in a character and merged Hijja with AHCD, which achieved an average prediction accuracy of 96%.

Using Convolutional Neural Networks, Arabic Handwritten Character Recognition

Using Convolutional Neural Networks, Arabic Handwritten Character Recognition

Combination of machine learning algorithms and Resnet50 for Arabic Handwritten Classification

The recognition or classification of Arabic handwritten characters is extremely crucial in many applications and, at the same time, one of the biggest challenges that machine learning faces. The emergence of deep learning, particularly Convolutional Neural Networks (CNN), is considered a suitable technique to face these challenges. In this research paper, an investigation model is proposed to make recognition for Arabic handwriting utilizing one of CNN architectures: ResNet50 architecture, after replacing the last layer with one of two types of machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to reduce training time and increase overall accuracy. Our experimental work was performed on three data sets: Arabic Handwritten Character Dataset (AHCD), Alexa Isolated Alphabet Dataset (AIA9K), and Hijja Dataset. Experimental results show that combining ResNet50 with random forest produces more accurate and consistent results than the ResNet50 model produces by itself. Finally, the comparison with the other methods across all data sets demonstrates the robustness and effectiveness of the combination of random forest with the ResNet50 approach. Where the modified ResNet50 architecture has achieved a rate of 92.37%, 98.39%, and 91.64%, while the combination architecture has achieved 95%, 99%, and 92.4% for AIA9K, AHCD, and Hijja datasets, respectively.

OCR for Arabic character recognition with advanced principal component analysis based on feature extraction and fuzzy-KNN based classification

Offline character recognition has become a highly important study field for various pattern recognition applications in recent years. Several handwritten character recognition systems have been suggested, with the complexity of these systems varying depending on the recognizing units' writing styles. In reality, identifying letters or numerals is far simpler than recognizing cursive sentences or lines of text. As a result, early handwriting recognition algorithms could only distinguish a few characters with limited vocabularies. Nowadays Arabic handwritten character recognition is very important as it is very difficult to identify. The cursive writing and variety of styles make this recognition more complex. This paper presents an automated model for ACR. This ACR is constructed from four phases: Preprocessing, Segmentation, Feature Extraction, and Classification. In this research article, we compare the advanced EPSO EKNN Algorithm with the earlier DBN Algorithm and also suggest a new method having higher Accuracy. In this research article, the "Enhanced K-Nearest Neighbor" (EKNN) classification model was used to identify and classify or simply recognize the particular Arabic character, and the "Extended Particle Swarm Optimization" (EPSO) methodology was introduced to select the best feature from feature extraction to comply with Arabic-Character Classification.

Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition

Abstract Currently, Arabic character recognition remains one of the most complicated challenges in image processing and character identification. Many algorithms exist in neural networks, and one of the most interesting algorithms is called generative adversarial networks (GANs), where 2 neural networks fight against one another. A generative adversarial network has been successfully implemented in unsupervised learning and it led to outstanding achievements. Furthermore, this discriminator is used as a classifier in most generative adversarial networks by employing the binary sigmoid cross-entropy loss function. This research proposes employing sigmoid cross-entropy to recognize Arabic handwritten characters using multi-class GANs training algorithms. The proposed approach is evaluated on a dataset of 16800 Arabic handwritten characters. When compared to other approaches, the experimental results indicate that the multi-class GANs approach performed well in terms of recognizing Arabic handwritten characters as it is 99.7% accurate. Keywords: Generative Adversarial Networks (GANs), Arabic Characters, Optical Character Recognition, Convolutional Neural Networks (CNNs).

Character Recognition of Arabic Handwritten Characters Using Deep Learning

Optical character recognition (OCR) is used to digitize texts in printed documents and camera images. The most basic step in the OCR process is character recognition. The Arabic language is more complex than other alphabets, as the cursive is written in cursive and the characters have different spellings. Our research has improved a character recognition model for Arabic texts with 28 different characters. Character recognition was performed using Convolutional Neural Network models, which are accepted as effective in image processing and recognition. Three different CNN models have been proposed. In the study, training and testing of the models were carried out using the Hijja data set. Among the proposed models, Model C with a 99.3% accuracy rate has obtained results that can compete with the studies in the literature.

UnCNN: A New Directed CNN Model for Isolated Arabic Handwritten Characters Recognition

UnCNN: A New Directed CNN Model for Isolated Arabic Handwritten Characters Recognition

Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

In recent years, researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities. The recent developments of artificial intelligence (AI), natural language processing (NLP), and computational linguistics (CL) find useful in the analysis of regional low resource languages. Automatic lexical task participation might be elaborated to various applications in the NLP. It is apparent from the availability of effective machine recognition models and open access handwritten databases. Arabic language is a commonly spoken Semitic language, and it is written with the cursive Arabic alphabet from right to left. Arabic handwritten Character Recognition (HCR) is a crucial process in optical character recognition. In this view, this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer (CLDL-THRSS) for Indigenous Language. The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition. Firstly, the automated handwriting recognition procedure involves preprocessing, segmentation, feature extraction, and classification. Also, the Capsule Network (CapsNet) based feature extractor is employed for the recognition of handwritten Arabic characters. For optimal hyperparameter tuning, the cuckoo search (CS) optimization technique was included to tune the parameters of the CapsNet method. Besides, deep neural network with hidden Markov model (DNN-HMM) model is employed for the automatic speech synthesizer. To validate the effective performance of the proposed CLDL-THRSS model, a detailed experimental validation process takes place and investigates the outcomes interms of different measures. The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.

Offline Arabic handwritten character recognition: from conventional machine learning system to deep learning approaches

Researchers have made great strides in the area of Arabic handwritten character recognition in the last decades especially with the fast development of deep learning algorithms. The characteristics of Arabic manuscript text pose several problems for a recognition system. This paper presents a conventional machine learning system based on the extraction of a set of preselected features and an SVM classifier. In the second part, a simplified convolutional neural network (CNN) model is proposed, which is compared to six other CNN models based on the pre-trained architectures. The suggested methods were tested using three databases: two versions of the OIHACDB dataset and the AIA9K dataset. The experimental results show that the proposed CNN model obtained promising results, as it is able to recognise 94.7%, 98.3%, and 95.6% of the test set of the three databases OIHACDB-28, OIHACDB-40, and AIA9K, respectively.

Offline Arabic handwritten character recognition: from conventional machine learning system to deep learning approaches

Offline Arabic handwritten character recognition: from conventional machine learning system to deep learning approaches

Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks

Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks

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