Arabic Handwriting Recognition Research Articles

Machine Learning Approach for Arabic Handwritten Recognition

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

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.

Enhancing arabic handwriting recognition through optimized deep learning frameworks

Detecting Arabic handwriting is challenging due to letter shapes, intervening segments, and diacritical marks, despite recent advances in pattern recognition. Deep learning architectures ConvNeXt and NFNet-F5 and the meta-heuristic optimization algorithm Aquila Optimizer, inspired by eagle hunting, are used to overcome these challenges. We first review Arabic handwriting recognition literature to determine strengths, weaknesses, and future directions. Next, we describe Arabic handwriting, particularly its interconnectivity, diversity, and many diacritical symbols that make recognition difficult. The Aquila Optimizer is used to optimize CNN hyperparameters in this paper. The goal is to increase the recognition rate and reduce the computational workload. To prove the Aquila Optimizer's efficacy, the ConvNeXt and NFNet-F5 topologies are compared with and without it. The optimized ConvNeXt model has a recognition rate of 98.96%, significantly higher than comparable techniques. It also thoroughly assesses numerous meta-heuristic optimizers and highlights the Aquila Optimizer's potential to improve model performance. This work has enhanced Arabic handwriting recognition by building more precise and efficient models and provides a framework for future research on optimization and applying these models to other scripts like Persian and Urdu.

End-to-End Deep Learning Framework for Arabic Handwritten Legal Amount Recognition and Digital Courtesy Conversion

Arabic handwriting recognition and conversion are crucial for financial operations, particularly for processing handwritten amounts on cheques and financial documents. Compared to other languages, research in this area is relatively limited, especially concerning Arabic. This study introduces an innovative AI-driven method for simultaneously recognizing and converting Arabic handwritten legal amounts into numerical courtesy forms. The framework consists of four key stages. First, a new dataset of Arabic legal amounts in handwritten form (“.png” image format) is collected and labeled by natives. Second, a YOLO-based AI detector extracts individual legal amount words from the entire input sentence images. Third, a robust hybrid classification model is developed, sequentially combining ensemble Convolutional Neural Networks (CNNs) with a Vision Transformer (ViT) to improve the prediction accuracy of single Arabic words. Finally, a novel conversion algorithm transforms the predicted Arabic legal amounts into digital courtesy forms. The framework’s performance is fine-tuned and assessed using 5-fold cross-validation tests on the proposed novel dataset, achieving a word level detection accuracy of 98.6% and a recognition accuracy of 99.02% at the classification stage. The conversion process yields an overall accuracy of 90%, with an inference time of 4.5 s per sentence image. These results demonstrate promising potential for practical implementation in diverse Arabic financial systems.

DeepAHR: a deep neural network approach for recognizing Arabic handwritten recognition

DeepAHR: a deep neural network approach for recognizing Arabic handwritten recognition

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.

Offline Recognition System for Arabic Handwritten Words Using Artificial Neural Networks

Arabic handwritten recognition is playing an important role in communicating and documenting information in everyday life. Handwriting has functional importance considering its ubiquity in human activities, as in reading handwritten notes in a PDA (Personal Digital Assistant), in postal addresses on envelopes, in amounts in bank accounts, etc. In this work, an offline recognition system for Arabic handwritten names is proposed using Artificial Neural Network (ANN). As part of the feature extraction, region props were used, it extracts array with fields: Area, Centroid and Bounding Box. A feed-forward neural network with three different topologies was trained using backpropagation algorithm. The number of nodes in the hidden layer was varied; the lower the number of hidden layers, the higher the recognition rate. Moreover, we found that the first topology (with two hidden layers) produced a higher recognition rate 92% from the trained data compared to the second and third topologies that produced a smaller recognition rate.

Convolutional Arabic handwriting recognition system based BLSTM-CTC using WBS decoder

Arabic handwriting recognition (AHR) poses major challenges for pattern recognition due to the cursive script and visual similarity of Arabic characters. While deep learning demonstrates promise, architectural enhancements may further improve performance. This study presents an offline AHR approach using a convolutional neural network (CNN) with bidirectional long short-term memory (BLSTM) and connectionist temporal classification (CTC). By enhancing temporal modeling and context representations without segmentation requirements, this BLSTM-CTC-CNN framework with an integrated Word Beam Search (WBS) decoder achieved 94.58% accuracy on the IFN/ENIT database. Results highlight improved efficiency over prior works. This demonstrates continued advancement in sophisticated deep learning techniques for accurate AHR through specialized modeling of Arabic script cursive properties and decoding constraints. This research represents an advancement in the continuous development of progressively intricate and precise systems for handwriting recognition.

Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition

Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to achieve better recognition results. On the other hand, Multilayer Perceptron (MLP) is one of the most common Artificial Neural Networks (ANNs) which deals with various problems efficiently. Therefore, this study introduces a new technique called Block Density and Location Feature (BDLF) with MLP, namely BDLF-MLP, which aims to extract novel features from letter images and estimate the letter's pixel density and its location for each equal-sized block in the image. In other words, BDLF-MLP can deal with various styles of Arabic handwritten, such as overlapping letters. The BDLF-MLP starts with the Block Feature Extraction (BFE) of the image by dividing the image into sixteen parts. After that, it calculates the density and location of each block (i.e., BDLF) by finding the sum of all values inside blocks. Finally, it determines the position of the greatest pixel density to obtain better recognition accuracy. The dataset containing 720 images is used to evaluate the efficiency of the proposed technique. Also, 1440 letters are used for training and testing divided evenly between them. The experiment results illustrate that BDLF-MLP outperformed the other algorithms in the literature with an accuracy of 97.26 %.

Arabic Character Recognition Using CNN LeNet-5

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.

Classification and Localization of Arabic Handwritten Text in KHATT Dataset Based on Faster R-CNN

Arabic handwriting recognition is an important research area in computer vision. Due to the complexity of the Arabic script, this is an arduous task. Several approaches have been proposed to address this challenge, including deep learning algorithms. Despite their efficiency, these approaches present some limitations such as the use of lexicon-driven models, the need of a lot of data for training and the huge computational cost. We propose, in this paper, two novel models based on the robust Faster Region-Convolution Neural Network (Faster R-CNN) to recognize Arabic handwritten sentences. The Faster R-CNN is commonly used to detect objects in images and depends on region proposal algorithms. These models use the pre-trained VGG 16 and ResNet50. Moreover, they use a soft non-maximum suppression strategy (Soft-NMS) in the post-processing stage instead of the traditional NMS to increase the detection rate of overlapping items. The models will be trained independently to identify words in the text lines and tested using sentences from the (KFUPM Handwritten Arabic TexT) KHATT dataset. It will be shown that the faster R-CNN models allow greater accuracy and effectiveness in handwriting recognition. They achieve accuracy rates of 99% and 98% for the two networks, VGG16 and ResNet50, respectively

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

Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab

Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.63%, validation accuracy is 91.82%, and the testing accuracy is 95.03%.

An Effective Combination of Convolutional Neural Network and Support Vector Machine Classifier for Arabic Handwritten Recognition

An Effective Combination of Convolutional Neural Network and Support Vector Machine Classifier for Arabic Handwritten Recognition

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%.

Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System

Around 27 languages and more than 420 million people worldwide use Arabic letters. That makes the Arabic language one of the most used languages. However, the Arabic language has a challenge, namely the difference in letters based on their position. Arabic handwriting recognition is important for various applications, such as education and communication. One example is during a pandemic when most education has turned digital, making recognizing students' Arabic handwriting difficult. This paper aims to create a model that can recognize Arabic handwriting by comparing several CNN architectures using transfer learning to classify Arabic, Hijja, and AHCD handwriting datasets. Transfer learning is a model that has been trained by previous datasets to other datasets and is suitable for use in models with small datasets because it can improve model accuracy even with small datasets. The datasets were split into 60%, 20%, and 20% for training, validation, and testing. Each model uses data augmentation and 50% dropout on a fully connected layer to reduce overfitting. Some of the CNN architectures used in this study to create Arabic writing recognition models are ResNet, DenseNet, VGG16, VGG19, InceptionV3, and MobileNet. The models were compiled and trained with various parameters. The best model achieved to classify AHCD and Hijja dataset is VGG16 with Adam optimizer and 0.0001 learning rate. Based on this research, it is expected to know the performance of the best model for classifying Arabic handwriting.

Arabic Handwritten Recognition Using Hybrid CNN, HMM and an Intelligent Network Based on Dentate Gyrus of the Brain

Arabic Handwritten Recognition Using Hybrid CNN, HMM and an Intelligent Network Based on Dentate Gyrus of the Brain

Towards Accurate Children’s Arabic Handwriting Recognition via Deep Learning

Automatic handwriting recognition has received considerable attention over the past three decades. Handwriting recognition systems are useful for a wide range of applications. Much research has been conducted to address the problem in Latin languages. However, less research has focused on the Arabic language, especially concerning recognizing children’s Arabic handwriting. This task is essential as the demand for educational applications to practice writing and spelling Arabic letters is increasing. Thus, the development of Arabic handwriting recognition systems and applications for children is important. In this paper, we propose two deep learning-based models for the recognition of children’s Arabic handwriting. The proposed models, a convolutional neural network (CNN) and a pre-trained CNN (VGG-16) were trained using Hijja, a recent dataset of Arabic children’s handwriting collected in Saudi Arabia. We also train and test our proposed models using the Arabic Handwritten Character Dataset (AHCD). We compare the performance of the proposed models with similar models from the literature. The results indicate that our proposed CNN outperforms the pre-trained CNN (VGG-16) and the other compared models from the literature. Moreover, we developed Mutqin, a prototype to help children practice Arabic handwriting. The prototype was evaluated by target users, and the results are reported.

Temporal residual network based multi-head attention model for Arabic handwriting recognition

In this study, we developed a new system for online Arabic handwriting recognition based on temporal residual networks with multi-head attention model. The main idea behind the application of attention mechanism was to focus on the most relevant parts of the data by a weighted combination of all input sequences. Moreover, we applied beta elliptic approach to represent both kinematic and geometric aspects of the handwriting motion. This approach consists of representing the neuromuscular impulses involving during the writing act. In the dynamic profile, the curvilinear velocity can be fitted by an algebraic sum of overlapped beta functions, while the original trajectory can be rebuilt by elliptic arcs delimited between successive extremum velocity instants. The experiments were conducted on LMCA database containing the trajectory coordinates of 23141 Arabic handwriting letters, and showed very promising results that achieved the recognition rate of 97,12%