Arabic Recognition Research Articles

ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs

Motivated by the widespread increase in the phenomenon of code-switching between Egyptian Arabic and English in recent times, this paper explores the intricacies of machine translation (MT) and automatic speech recognition (ASR) systems, focusing on translating code-switched Egyptian Arabic-English to either English or Egyptian Arabic. Our goal is to present the methodologies employed in developing these systems, utilizing large language models such as LLama and Gemma. In the field of ASR, we explore the utilization of the Whisper model for code-switched Egyptian Arabic recognition, detailing our experimental procedures including data preprocessing and training techniques. Through the implementation of a consecutive speech-to-text translation system that integrates ASR with MT, we aim to overcome challenges posed by limited resources and the unique characteristics of the Egyptian Arabic dialect. Evaluation against established metrics showcases promising results, with our methodologies yielding a significant improvement of 56% in English translation over the state-of-the-art and 9.3% in Arabic translation. Since code-switching is deeply inherent in spoken languages, it is crucial that ASR systems can effectively handle this phenomenon. This capability is crucial for enabling seamless interaction in various domains, including business negotiations, cultural exchanges, and academic discourse. Our models and code are available as open-source resources.1,2.

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 %.

Holy Quran Arabic Character Recognition using Neural Networks

Arabic text recognition is one of the most important fields of optical character recognition for Arabic people. Arabic text recognition consider of converting an image of Arabic text into editable format such as doc or pdf. One of its branches is holy Quran text recognition. The key aim of this research is to develop Quran Arabic Recognition System. The study recognises the Arabic text (character) individually . In addition, this study will use Back propagation neural networks for character recognitions. Because of the nature of holy quran the rate of recognition must by extremely high. Keywords: text recognition, Arabic text recognition, quran text recognition neural networks, DOI: 10.7176/IKM/13-3-05 Publication date: May 31 st 2023

A Novel Hybrid DL Model for Printed Arabic Word Recognition based on GAN

The recognition of printed Arabic words remains an open area for research since Arabic is among the most complex languages. Prior research has shown that few efforts have been made to develop models of accurate Arabic recognition, as most of these models have faced the increasing complexity of the performance and lack of benchmark Arabic datasets. Meanwhile, Deep learning models, such as Convolutional Neural Networks (CNNs), have been shown to be beneficial in reducing the error rate and enhancing accuracy in Arabic character recognition systems. The reliability of these models increases with the depth of layers. Still, the essential condition for more layers is an extensive amount of data. Since CNN generates features by analysing large amounts of data, its performance is directly proportional to the volume of data, as DL models are considered data-hungry algorithms. Nevertheless, this technique suffers from poor generalisation ability and overfitting issues, which affect the Arabic recognition models' accuracy. These issues are due to the limited availability of Arabic databases in terms of accessibility and size, which led to a central problem facing the Arabic language nowadays. Therefore, the Arabic character recognition models still have gaps that need to be bridged. The Deep Learning techniques are also to be improved to increase the accuracy by manipulating the strength of technique in a neural network for handling the lack of datasets and the generalisation ability of the neural network in model building. To solve these problems, this study proposes a hybrid model for Arabic word recognition by adapting a deep convolutional neural network (DCNN) to work as a classifier based on a generative adversarial network (GAN) work as a data augmentation technique to develop a robust hybrid model for improving the accuracy and generalisation ability. Each proposed model is separately evaluated and compared with other state-of-the-art models. These models are tested on the Arabic printed text image dataset (APTI). The proposed hybrid deep learning model shows excellent performance regarding the accuracy, with a score of 99.76% compared to 94.81% for the proposed DCNN model on the APTI dataset. The proposed model indicates highly competitive performance and enhanced accuracy compared to the existing state-of-the-art Arabic printed word recognition models. The results demonstrate that the generalisation of networks and the handling of overfitting have also improved. This study output is comparable to other competitive models and contributes an enhanced Arabic recognition model to the body of knowledge.

A REVIEW ON ARABIC HANDWRITING RECOGNITION

Handwriting recognition is considered a very hard area of research, especially for Arabic, because of its ligatures, cursive nature, diacritics, and overlapping. Although many studies have been conducted on Arabic recognition, this field still has many unsolved problems. This work aims to provide a comprehensive review of various strategies for handling Arabic handwriting recognition. Furthermore, it details handwriting recognition, general recognition, Arabic recognition, its characteristics, and the difficulties it faces. Additionally, we discuss online and offline Arabic recognition and other classifications of Arabic recognition methods. We also highlight efforts related to the Arabic datasets and the most important ones, such as the first online Quranic handwritten word dataset. Moreover, we address other efforts related to Arabic recognition that don't deal with the recognition process itself, such as estimating the dates of historical Arabic documents.

ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model

When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy.

ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Model

The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%.

Last Teen Pixels for Arabic Font Size and Style Recognition

Arabic printed script segmentation and recognition techniques change from font to other i.e. each font has particular properties calligraphic and structural which differ with other. Majority of segmentation system suffer in word or sub word segmentation into characters because they consider one algorithm to segment all kind of Arabic printed font, style and size. The goal of this work is to prepare a system of word or sub word Optical Font Arabic Recognition (OFAR) for different font size and style of Arabic printed script, in order to integrate it in global Arabic Optical Character Recognition (AOCR) to choose preferred and good segmentation algorithm. APTI database was used to extract last ten pixels for each word or sub word to build new database of last 10 pixels for each word; OFAR is based upon this new database and our extraction approach called Pixels Continuity (PC) algorithm in different matrix direction and some histogram statistics to extract 20 features. Three KNN classifiers with K=5 and three different distances using Cityblock, Euclidean and Correlation based upon majority-vote are used to evaluate the system robustness. This classifier is compared in the first time with Back propagation Neural Network and Steerable Pyramid (SP) algorithm to re cognize three font families, then in the second time with Gaussian Mixture Models (GMMs) to recognize font and size. The average recognition results obtained was 99.55% about font and size and 98.17% for font, size and style recognition.

Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.

A Novel Approach to Spoken Arabic Number Recognition Based on Developed Ant Lion Algorithm

Intelligent spoken system is constructed to recognize numbers spoken in Arabic language by different people. Series of operations are performed on audio sound file as pre-processing stages. A novel approach is applied to extract features of audio files called Max Mean Log to reduce audio file dimensions in an efficient manner. Several stages of initial processing are used to prepare the file for the next step of the recognition process. The recognition process begins with the use of Antlion’s advanced intelligence algorithm to determine the type of the spoken number in Arabic and later convert it to a visual text that represents the value of the spoken number. The current proposal method is relatively fast and very effective. The percentage of recognizing numbers spoken by the proposed algorithm is 99%. For 1,800 different audio files, the error rate was 1%. Additional 40 audio files were used that are different from people’s original dataset. Due to an additional examination of the system and its ability to recognize the audio file, the rate of discrimination for such files was 72.5%.

Novel Algorithm Utilizing Deep Learning for Enhanced Arabic Lip Reading Recognition

Novel Algorithm Utilizing Deep Learning for Enhanced Arabic Lip Reading Recognition

Hijaiyah Letter Segmentation Using Connected Component Labeling Method

Arabic letters or Hijaiyah letters recognition is a challenge in itself because one letter consists of more than one character, namely the main character, companion character such as dots and lines, and punctuation called harakat. The image segmentation process is the most important in a character recognition system because it affects the separation of objects in an image. In this research, Hijaiyah letter segmentation aims to separate the letters according to the character of each letter using the Connected Component Labeling (CCL) method. Merging labels on each character will be done by looking for the Euclidean distance value from adjacent centroids. The experiment succeeded in segmenting each Hijaiyah character with an accuracy value of 86%.

A novel comprehensive database for arabic and english off-line handwritten digits recognition

<span>The recognition of Arabic handwritten is received at the same interest as other Latin languages. In Optical Character Recognition (OCR), handwriting Arabic recognition is considered as one of the critical and difficult tasks in the various scientific area. The main issues of this matter were due to the lack of public Arabic handwriting databases and the cursive nature of Arabic writing. In this paper, a new benchmark database is built for the Arabic and English off-line handwritten digits Recognition. The original form is divided into three groups: Arabic digits, English digits, and word Arabic digits which written five times by 100 different academic staff and students of university writers. Our database contains 14500 images; divided into two subsets of training and testing to help researchers through evaluating and comparing results obtained from their systems. </span>

Arabic speaker recognition system based on phoneme fusion

With the increasing number and popularity of smart devices in the past few decades, especially those that execute different types of health-related applications, their security is a growing concern. Biometric authentication can be used for device security, and speaker recognition (SR) is one of its elegant forms. In this study, an SR system is developed for the security of smart devices, which identifies a person from the shortest utterance of Arabic language, achieving the highest SR rate, by using the benchmark Linguistic Data Consortium (LDC) Arabic dataset. This study focuses on the use of the consonants and vowels of the Arabic language. It is observed that certain consonants or a fusion of certain consonants in Arabic language helps to ascertain the speaker’s identity efficiently. The shortest utterance that better presents the speaker’s identity by using such consonants is revealed. The system was analyzed by using different numbers of consonants and achieved the maximum (100%) SR rate using a fusion of only three consonants of the Arabic language. These consonants or their combinations can be used to develop text for any speaker-based authentication system.

ARABIC SPEAKER RECOGNITION SYSTEM USING GAUSSIAN MIXTURE MODEL AND EM ALGORITHM

Arabic language is a semantic language that has complicated difficulties when compared to English and other languages. In this paper an Arabic speaker recognition system has been developed for introducing conversion of the uttered Arabic speaker instantly after the utterance. The voice samples were recorded, the pre-processing activity detected to evaluate the voice parts from unvoiced, framing and rectangular window slides techniques has been used for segmentation of the Arabic Speech signals, followed by Mel Frequency Spectrum Coefficients (MFCC) for features extractions, The feature vectors are grouped for each spoken sample using VQLBG Algorithm and Gaussian Mixer Model (GMM) applied for classification and recognition an unknowing speaker through his uttered words which belong to specific cluster that is differenced form others clusters related to others Arabic speakers. This approach reported in providing 95.5% of recognition rate.

A Silent Password Recognition Framework Based on Lip Analysis

Securing passwords either written or spoken is considered one of the challenging authentication issues faced by individuals and organizations. Written passwords could be easily stolen by look over, or man-behind, while spoken passwords could be recorded and replayed by attackers. A proposed silent password which is based on a dual security model for lip movement analysis will be a promoting solution to these attacks. The goal of the current research is to propose a hybrid voting framework for silent passwords recognition using lip movement analysis. The proposed framework is built for Arabic language by extracting figures from predefined Arabic lexicon. The predefined lexicon mainly contains the Arabic figures from zero to nine with different shapes. The framework takes a video or sequence of images as an input, and outputs the corresponding silent password for the frames extracted from the input video. In this paper, three techniques will be employed to extract effective visual features from mouth-lip movement. Such techniques are SURF, HoG and Haar feature extractor. The resultant features in each technique are fed separately into a classification model, namely, the hidden Markov model (HMM). The HMM identifies corresponding Arabic figure from a predefined lexicon based on input features. The final classification models that are produced from the three techniques have been grouped in a voting scheme to produce the final classification result. The proposed model will be tested on handcrafted data set of lip movement, and it has shown a promising result with improved accuracy of Arabic figures recognition.

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.

A Canonicalization of Distinctive Phonetic Features to Improve Arabic Speech Recognition

The robustness of speech classification and recognition systems can be improved by the adoption of language distinctive phonetic feature (DPF) elements that can increase the effective characterization of a speech signal. This paper presents the results of applying Hidden Markov Models (HMMs) that perform Arabic phoneme recognition in conjunction with the inclusion and classification of their DPF element classes. The research focuses on classifying Modern Standard Arabic (MSA) phonemes within isolated words without a language context. HMM-based phoneme recognition is tested using 8, 16, and 32 HMM Gaussian mixture models. The monophone configuration is designed with consideration of 2-gram language model to evaluate the inherent performance of the system. The overall correct rates for classifying DPF element classes for the three versions of HMM systems are 83.29% 88.96%, and 92.70% for 8, 16, and 32 HMM Gaussian mixture model systems, respectively.

Design of arabic recognition application using Convolutional neural network

Design of arabic recognition application using Convolutional neural network

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.