Arabic Handwriting Recognition System Research Articles

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.

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.

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.

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.

Arabic handwriting recognition system using convolutional neural network

Automatic handwriting recognition is an important component for many applications in various fields. It is a challenging problem that has received a lot of attention in the past three decades. Research has focused on the recognition of Latin languages’ handwriting. Fewer studies have been done for the Arabic language. In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja. Our dataset contains 47,434 characters written by 591 participants. In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN). We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset. Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature.

Improving the DBLSTM for on-line Arabic handwriting recognition

Various applications involved in the computer recognition of pen-input handwritten words, such as the online form filling, text editing, note taking, and so on. Therefore, a great deal of research work tries to improve the recognition rate of those online words recognition systems resulting in several effective methods. Relevant results related to Latin and Chinese scripts have been achieved. However, the Arabic script has been neglected so far, which stimulated us to propose a new online Arabic handwriting recognition system based on DBLSTM that relies on three techniques that were applied in order to enhance its performance. First, the dropout was applied, in different positions, to prevent overfitting. Then, ReLU and Maxout units were added, in different ways, to overcome the vanishing gradient problem. These proposed systems were tested on a large database ADAB to prove its performance against difficult conditions such as the variety of writers, the large vocabulary and the diversity of style. According to the experimental results and compared to the baseline system, the best tested architecture gives a reduction of 10.99% in label error rate.

Developing an Arabic Handwritten Recognition System by Means of Artificial Neural Network

Developing an Arabic Handwritten Recognition System by Means of Artificial Neural Network

Out of vocabulary word detection and recovery in Arabic handwritten text recognition

Today's Arabic Handwriting recognition systems are able to recognize arbitrary words over a large but finite vocabulary. Systems operating with a fixed vocabulary are bound to encounter so-called out-of-vocabulary (OOV) words. The aim of this research is to propose a two-step approach that tackles the problem of OOV words in Arabic handwriting. In the first step, we exploit different types of sub-word units to detect the potential OOVs. In the recovery stage, a dynamic dictionary is built to extend the initial static word lexicon in order to cope with the detected OOVs. The recovery includes a selection step in which the best word candidates extracted from the external resource are kept. Experiments were conducted on the public benchmarking KHATT and AHTID/MW databases. The obtained results revealed that sub-word modeling could give cues for improving the detection and that the use of a dynamic dictionary significantly improves the recognition performance compared to one-step approaches that are based on a large static dictionary or the combination of different sub-word units. We achieve the state of the art results on the KHATT dataset.

Contextual Arabic Handwriting Recognition System using Embedded Training based Hybrid HMM/MLP Models

Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems.The proposed work reports an effective method on improvement our previous work that takes into consideration the context of character by applying an embedded training based HMMs this HMM is enhanced by anArtificial neural network that are incorporated into the process of classification to estimate the emission probabilities. The experiments are done on the same benchmark IFN/ENIT database of our previous work to compare the results and show the effectiveness of hybrid classifier for enhancing the recognition rate the results are promising and encouraging.

Hybrid modelling of an off line Arabic handwriting recognition system: results and evaluation

In this paper, we presented a state of the art in the field of Arabic handwriting recognition as well as the techniques used. Then, we detailed the general architecture of an Arabic Handwriting Recognition System (AHRS) and the contributions that we have proposed at each phase: first, an analytical segmentation approach based on a morphological/structural analysis of the entire word and sub word; then, a combination of different features to extract the relevant information from text image; thereafter, a hybrid classification approach using long short term memory recurrent neural network (LSTM-RNN) and different other statistical classifiers to learn many characters shapes simultaneously and to improve the performance of our proposed system; and finally, a post-processing approach to reconstruct the characters, words and lines of the text using Morpho-Syntactic analyser 'AlKhalil Morph Sys'. Our proposed system has shown promising results to solve the difficulties of Arabic handwriting recognition.

Hybrid modelling of an off line Arabic handwriting recognition system: results and evaluation

Hybrid modelling of an off line Arabic handwriting recognition system: results and evaluation

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join). In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP) is used according to the first vision; whereas Local Binary Patterns (LBP) are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB) and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%.

An Efficient Image Thresholding Method for Arabic Handwriting Recognition System

Image preprocessinghas assumed an essential part ofhandwriting recognition system. The main primary stage of the image preprocessing is thresholding.Aneffectivethresholdingmethodis based on Fuzzy C-Means clustering (FCM) for Arabic Handwriting Recognition system (AHR) has been proposed in this paper. Since thresholding stage in AHR isimperative to reduce the dimensionality of image to remove the undesirableinformation (noise)then increase the processing speed of the AHR system. The algorithm is performing by feedingthe intensity of the pixel value of the image pixels into the FCM clustering algorithm. Exploratory results with artificial and real life images show thatthe proposed method gives better accuracy and good efficiency than the current methods.

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.

An Arabic handwriting synthesis system

In this paper, we present an Arabic handwriting synthesis system. Two concatenation models to synthesize Arabic words from segmented characters are adopted: Extended-Glyphs connection and Synthetic-Extensions connection. We use our system to synthesize handwriting from a collected dataset and inject it into an expanded dataset. We experiment by training a state-of-the-art Arabic handwriting recognition system on the collected dataset, as well as on the expanded dataset, and test it on the IFN/ENIT Arabic benchmark dataset. We show significant improvement in recognition performance due to the data that was synthesized by our system.

Arabic Handwriting Text Offline Recognition Using the HMM Toolkit (HTK)

This Recognition of Arabic text handwritten awaits precise recognition solutions. There are a lot of difficulties facing a good handwritten Arabic recognition system such as unlimited variant in human handwriting, similarities of different character shapes, and their location in the word. This paper presents a handwriting Arabic text recognition system. It decomposes the text image into text line images and extracts a set of simple statistical features from a narrow window which is sliding a long that text line, then it injects the resulting feature vectors to the Hidden Markov Model Toolkit (HTK). HTK is a portable toolkit for speech recognition system. In recognized state, the concatenation of characters to form words is modelled by simple lexical models, each word is modelled by a stochastic finite-state automaton (SFSA), and the concatenation of words into sentences is modelled by an n-gram language model. The proposed system is applied to a data corpus constructed by Text lines examples from the “Arabic-Numbers”, which contains 1905 sentences and 47 words. This phrase is written by 5 different peoples.

An effective approach to offline Arabic handwriting recognition

Segmentation is the most challenging part of Arabic handwriting recognition due to the unique characteristics of Arabic writing that allow the same shape to denote different characters. An Arabic handwriting recognition system cannot be successful without using an appropriate segmentation method. In this paper, a very effective and efficient off-line Arabic handwriting recognition approach is proposed. The proposed approach has three stages. Firstly, all characters are simplified to single-pixel-thin images that preserve the fundamental writing characteristics. Secondly, the image pixels are normalized into horizontal and vertical lines only. Therefore, the different writing styles can be unified and the shapes of characters are standardized. Finally, these orthogonal lines are coded as unique vectors; each vector represents one letter of a word. To evaluate the proposed techniques, we have tested our approach on two different datasets. Our experimental results show that the proposed approach has superior performance over the state-of-the-art approaches.

Effective technique for the recognition of offline Arabic handwritten words using hidden Markov models

In this paper, we present a novel segmentation-free Arabic handwriting recognition system based on hidden Markov model (HMM). Two main contributions are introduced: a new technique for dividing the image into nonuniform horizontal segments to extract the features and a new technique for solving the problems of the skewing of characters by fusing multiple HMMs. Moreover, two enhancements are introduced: the pre-processing method and feature extraction using concavity space. The proposed system first pre-processes the input image by setting the thickness of the input word to three pixels and fixing the spacing between the different parts of the word. The input image is divided into constant number of nonuniform horizontal segments depending on the distribution of the foreground pixels. A set of robust features representing the gradient of the foreground pixels is extracted using sliding windows. The input image is decomposed into several images representing the vertical, horizontal, left diagonal and right diagonal edges in the image. A set of robust features representing the densities of the foreground pixels in the various edge images is extracted using sliding windows. The proposed system builds character HMM models and learns word HMM models using embedded training. Besides the vertical sliding window, two slanted sliding windows are used to extract the features. Three different HMMs are used: one for the vertical sliding window and two for the slanted windows. A fusion scheme is used to combine the three HMMs. The proposed system is very promising and outperforms all the other Arabic handwriting recognition systems reported in the literature.

Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words

Artificial neural networks have the abilities to learn by example and are capable of solving problems that are hard to solve using ordinary rule-based programming. They have many design parameters that affect their performance such as the number and sizes of the hidden layers. Large sizes are slow and small sizes are generally not accurate. Tuning the neural network size is a hard task because the design space is often large and training is often a long process. We use design of experiments techniques to tune the recurrent neural network used in an Arabic handwriting recognition system. We show that best results are achieved with three hidden layers and two subsampling layers. To tune the sizes of these five layers, we use fractional factorial experiment design to limit the number of experiments to a feasible number. Moreover, we replicate the experiment configuration multiple times to overcome the randomness in the training process. The accuracy and time measurements are analyzed and modeled. The two models are then used to locate network sizes that are on the Pareto optimal frontier. The approach described in this paper reduces the label error from 26.2% to 19.8%.

An Effective Approach to Offline Arabic Handwriting Recognition

Segmentation is the most challenging part of the Arabic handwriting recognition, due to the unique characteristics of Arabic writing that allows the same shape to denote different characters. In this paper, an off-line Arabic handwriting recognition system is proposed. The processing details are presented in three main stages. Firstly, the image is skeletonized to one pixel thin. Secondly, transfer each diagonally connected foreground pixel to the closest horizontal or vertical line. Finally, these orthogonal lines are coded as vectors of unique integer numbers; each vector represents one letter of the word. In order to evaluate the proposed techniques, the system has been tested on the IFN/ENIT database, and the experimental results show that our method is superior to those methods currently available.