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Journal articles on the topic 'Arabic handwriting digit Recognition'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Tutar, Mehmet. "Comparison of Handwritten Recognition Methods on Arabic and Latin Characters." Journal of Studies in Science and Engineering 2, no. 3 (2022): 22–30. http://dx.doi.org/10.53898/josse2022232.

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In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 test

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2

Ahmed, Subhi Abdalkafor, Kareem Awad Waleed, and M. Ali Alheeti Khattab. "A novel comprehensive database for Arabic and English off-line handwritten digits recognition." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 145–49. https://doi.org/10.11591/ijeecs.v20.i1.pp145-149.

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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 Arabi

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Subhi Abdalkafor, Ahmed, Waleed Kareem Awad, and Khattab M. Ali Alheeti. "A novel comprehensive database for arabic and english off-line handwritten digits recognition." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 145. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp145-149.

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<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, an

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4

Qasim, Sarah Salman, and Safa Hussein Oleiwi. "Advancing Arabic Handwritten Digit Recognition with AI-Enhanced Neural Network Architectures." Babylonian Journal of Artificial Intelligence 2024 (November 28, 2024): 146–57. https://doi.org/10.58496/bjai/2024/016.

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Neural network model developed in this paper aims at classification of the hand written digits using the data set from Arabic Handwritten Digits Dataset (AHDD). It also includes data preprocessing, model design, training, validating, hyperparameter optimisation, and comparison methodologies of the project. Some preprocessing included scaling of pixel intensity and data augmentation to improve variation, as well as data separation between training and validation. proposed architecture of the model were updated through adding of dropout layers as a form of regularization, tuning of the quantity

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AL-Saffar, Ahmed, Suryanti Awang, Wafaa AL-Saiagh, Ahmed Salih AL-Khaleefa, and Saad Adnan Abed. "A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN." Sensors 21, no. 21 (2021): 7306. http://dx.doi.org/10.3390/s21217306.

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Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the

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Surendra, Kumar Shukla POONAM VERMA. "Using Convolutional Neural Networks, Arabic Handwritten Character Recognition." Scandinavian Journal of Information Systems 34, no. 2 (2023): 139–44. https://doi.org/10.5281/zenodo.7885780.

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Recognizing handwritten Arabic numbers is a challenging research topic. Impulsive by this research topic proposed two convolutional neural networks for recognizing Arabic handwritten numerals. Two proposed models have been analyzed using different filter sizes. The Arabic Number dataset exported from Kaggle was trained. The simplest proposed model achieved high recognition accuracy of 99.92%, outperforming the other complex with a more reasonable accuracy. For the MADBase dataset

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Ahmed, Rami, Mandar Gogate, Ahsen Tahir, et al. "Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts." Entropy 23, no. 3 (2021): 340. http://dx.doi.org/10.3390/e23030340.

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we introduce a novel context-aware model based on deep neural networks to address the ch

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Xiaolin, Wang, Du Yingcui, Dong Shulong, Li Chenchen, and Fu Yuan. "Handwritten digital recognition based on SVM, KNN, MLP, and Logistic Regression models." Journal of Scientific and Engineering Research 8, no. 7 (2021): 81–89. https://doi.org/10.5281/zenodo.10608782.

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<strong>Abstract</strong> As the only universal symbol in the world, Arabic numerals play an irreplaceable role in all walks of life. With the development of science and technology, more and more data information needs to be input into the computer and then processed. Because of the low efficiency, the method of manual identification of numbers on paper is not suitable for the identification of massive data. As a branch of optical character recognition technology, the main research content of handwritten numeral recognition is how to use computer to automatically recognize Arabic numerals writ

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Youssef, Nahla Ibrahim, and Nadia Abd-Alsabour. "A REVIEW ON ARABIC HANDWRITING RECOGNITION." Journal of Southwest Jiaotong University 57, no. 6 (2022): 745–64. http://dx.doi.org/10.35741/issn.0258-2724.57.6.66.

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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 oth

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Bin Durayhim, Anfal, Amani Al-Ajlan, Isra Al-Turaiki, and Najwa Altwaijry. "Towards Accurate Children’s Arabic Handwriting Recognition via Deep Learning." Applied Sciences 13, no. 3 (2023): 1692. http://dx.doi.org/10.3390/app13031692.

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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 i

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Abdleazeem, Sherif, and Ezzat El-Sherif. "Arabic handwritten digit recognition." International Journal of Document Analysis and Recognition (IJDAR) 11, no. 3 (2008): 127–41. http://dx.doi.org/10.1007/s10032-008-0073-5.

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Al-Helali, Baligh M., and Sabri A. Mahmoud. "Arabic Online Handwriting Recognition (AOHR)." ACM Computing Surveys 50, no. 3 (2017): 1–35. http://dx.doi.org/10.1145/3060620.

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Beldjehem, Mokhtar. "A Granular Framework for Recognition of Arabic Handwriting." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 5 (2009): 512–19. http://dx.doi.org/10.20965/jaciii.2009.p0512.

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We propose a novel cognitively motivated unifying framework for Arabic handwriting recognition that takes into account the nature of the human reading process of Arabic handwriting. This Modular Granular Architecture tackles the problem by observing Arabic handwriting from both perceptual and linguistic points of view and hence analyzes the underlying input signal from different granularity levels. It is based on three levels of abstraction: a low granularity level that uses perceptual features called global visual indices, a medium granularity level that is the conventional recognition stage

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Mhaske, Anirudh, Atharv Joshi, Dattaram Kajrekar, Ruturaj Jugdar, and Prof Ajita Mahapadi. "Digit Recognition Using MNIST Dataset." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1862–65. http://dx.doi.org/10.22214/ijraset.2022.46930.

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Abstract: In this paper, we have performed handwritten digit recognition using MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main goal is to compare the accuracy of the above models along with their execution time to obtain the best possible model for digit recognition. Reliability of humans over machines has never been so high that from classifying objects in photographs to adding sound to silent movies can all be done using deep learning and machine learning algorithms. Similarly, handwriting recognition is o

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Alugunuri, Sai Sharan, Kaithapuram Vishal Reddy, Chevvula Shiva Kumar, and T. Bhavani Prasad. "Handwritten Digit Prediction Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 2040–43. http://dx.doi.org/10.22214/ijraset.2023.49884.

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Abstract: For many years, numerous methods have been used in extensive research on handwriting recognition. The capacity to create an effective algorithm that can recognise handwritten digits given by users via scanner, tablet, and other digital devices is at the core of the issue. The automatic processing of bank checks, postal addresses, and other sorts of data already makes substantial use of handwritten digit recognition. Computational intelligence methods like artificial neural networks used by several current systems. CNN and the MNIST data set will be used to complete this. Handwriting

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BIADSY, FADI, RAID SAABNI, and JIHAD EL-SANA. "SEGMENTATION-FREE ONLINE ARABIC HANDWRITING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 07 (2011): 1009–33. http://dx.doi.org/10.1142/s0218001411008956.

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Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our c

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Lorigo, L. M., and V. Govindaraju. "Offline Arabic handwriting recognition: a survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 5 (2006): 712–24. http://dx.doi.org/10.1109/tpami.2006.102.

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18

Chergui, Leila, and Maamar Kef. "SIFT descriptors for Arabic handwriting recognition." International Journal of Computational Vision and Robotics 5, no. 4 (2015): 441. http://dx.doi.org/10.1504/ijcvr.2015.072193.

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El Abed, Haikal, and Volker Märgner. "ICDAR 2009-Arabic handwriting recognition competition." International Journal on Document Analysis and Recognition (IJDAR) 14, no. 1 (2010): 3–13. http://dx.doi.org/10.1007/s10032-010-0117-5.

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El Abed, Haikal, Monji Kherallah, Volker Märgner, and Adel M. Alimi. "On-line Arabic handwriting recognition competition." International Journal on Document Analysis and Recognition (IJDAR) 14, no. 1 (2010): 15–23. http://dx.doi.org/10.1007/s10032-010-0124-6.

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Tagougui, Najiba, Monji Kherallah, and Adel M. Alimi. "Online Arabic handwriting recognition: a survey." International Journal on Document Analysis and Recognition (IJDAR) 16, no. 3 (2012): 209–26. http://dx.doi.org/10.1007/s10032-012-0186-8.

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22

A. Al-Hamadani, Ammar, Maad Kamal Al-Anni, Gamil R. S. Qaid, and Najran Nasser Hamood. "Improved Technique in Arabic Handwriting Recognition." Al-Iraqia Journal for Scientific Engineering Research 4, no. 2 (2025): 33–46. https://doi.org/10.58564/ijser.4.2.2025.316.

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Arabic handwriting recognition has significant applications in fields like postal sorting, handwritten text identification, and cheque processing. The process involves several steps: preprocessing, feature extraction, and classification. Preprocessing enhances image quality through noise removal, normalisation, and binarisation, which are essential for accurate segmentation. Feature extraction captures key information such as stroke direction and spatial relationships, which are crucial for distinguishing between different characters. Hybrid methods, statistical features, and structural featur

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Jemni, Sana Khamekhem, Yousri Kessentini, and Slim Kanoun. "Improving Recurrent Neural Networks for Offline Arabic Handwriting Recognition by Combining Different Language Models." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2052007. http://dx.doi.org/10.1142/s0218001420520072.

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In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We exten

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Soumia, Djelaila, Bendjillali Ridha Ilyas, Kamline Miloud, Mohammed Sofiane Bendelhoum, and Tadjeddine Ali Abderrazak. "Enhancing arabic handwriting recognition through optimized deep learning frameworks." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e7544. http://dx.doi.org/10.54021/seesv5n2-167.

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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 difficul

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Masruroh, Siti Ummi, Muhammad Fikri Syahid, Firman Munthaha, Asep Taufik Muharram, and Rizka Amalia Putri. "Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System." JOIV : International Journal on Informatics Visualization 7, no. 2 (2023): 330. http://dx.doi.org/10.30630/joiv.7.2.1605.

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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

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Aljuaid, Hanan, Dzulkifli Mohamad, and Muhammad Sarfraz. "Evaluation Approach of Arabic Character Recognition." International Journal of Computer Vision and Image Processing 1, no. 2 (2011): 58–77. http://dx.doi.org/10.4018/ijcvip.2011040105.

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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting

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Ahlawat, Savita, and Rahul Rishi. "A Genetic Algorithm Based Feature Selection for Handwritten Digit Recognition." Recent Patents on Computer Science 12, no. 4 (2019): 304–16. http://dx.doi.org/10.2174/2213275911666181120111342.

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Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost. Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature s

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Qomariyah, Fitriyatul, Fitri Utaminingrum, and Muchlas Muchlas. "Handwriting Arabic Character Recognition Using Features Combination." IJID (International Journal on Informatics for Development) 10, no. 2 (2021): 62–71. http://dx.doi.org/10.14421/ijid.2021.2360.

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The recognition of Arabic handwriting is a challenging problem to solve. The similarity among the fonts appears as a problem in the recognition processing. Various styles, shapes, and sizes which are personal and different across individuals make the Arabic handwriting recognition process even harder. In this paper, the data used are Arabic handwritten images with 101 sample characters, each of which is written by 15 different handwritten characters (total sample 101x15) with the same size (81x81 pixels). A well-chosen feature is crucial for making good recognition results. In this study, the

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Lochy, Aliette, Agnesa Pillon, Pascal Zesiger, and Xavier Seron. "Verbal structure of numerals and digits handwriting: New evidence from kinematics." Quarterly Journal of Experimental Psychology Section A 55, no. 1 (2002): 263–88. http://dx.doi.org/10.1080/02724980143000271.

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Two experiments used a digitizing tablet to analyse the temporal, spatial, and kinematic characteristics of handwritten production of arabic numbers. They addressed a specific issue of the numerical domain: Does the lexical and syntactic structure of verbal numerals influence the production of arabic numerals (Experiments 1 and 2), even after enforced semantic processing in a comparison task (Experiment 2)? Subjects had to write multi-digit arabic numerals (e.g., 1200) presented in two different verbal structures: a multiplicative one (e.g., teen-hundred, douze cents (twelve hundred)) or an ad

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Abuzaraida, Mustafa Ali, Mohammed Elmehrek, and Esam Elsomadi. "Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3584. http://dx.doi.org/10.11591/ijece.v11i4.pp3584-3592.

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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 D

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Mustafa, Ali Abuzaraida, Elmehrek Mohammed, and Elsomadi Esam. "Online handwriting Arabic recognition system using k-nearestneighbors classifier and DCT features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3584–92. https://doi.org/10.11591/ijece.v11i4.pp3584-3592.

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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 D

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Tamanna Sachdeva, Et al. "A Novel Approach for Hand-written Digit Classification Using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1627–35. http://dx.doi.org/10.17762/ijritcc.v11i9.9148.

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Humans' control over technology is at an all-time high, with applications ranging from visual object recognition to the dubbing of dialogue into silent films. Using algorithms for deep learning and machine learning. Similarly, the most crucial technologies are text line recognition fields of study and development, with an increasing number of potential outcomes. Handwriting recognition (HWR), also identified as Handwriting Text Acknowledgment, is the capacity of a computer to understand legibly handwritten input from bases such as paper documents, screens, and other devices. Evidently, we have

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Mars, Abdelkarim, and Georges Antoniadis. "Arabic Online Handwriting Recognition Using Neural Network." International Journal of Artificial Intelligence & Applications 7, no. 5 (2016): 51–59. http://dx.doi.org/10.5121/ijaia.2016.7504.

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Benbakreti, Soumia, Samir Benbakreti, Mohamed Benouis, and Ahmed Roumane. "Stacked autoencoder for Arabic handwriting word recognition." International Journal of Computational Science and Engineering 24, no. 6 (2021): 629. http://dx.doi.org/10.1504/ijcse.2021.10043725.

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Benbakreti, Samir, Mohamed Benouis, Ahmed Roumane, and Soumia Benbakreti. "Stacked autoencoder for Arabic handwriting word recognition." International Journal of Computational Science and Engineering 24, no. 6 (2021): 629. http://dx.doi.org/10.1504/ijcse.2021.119988.

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Ramdan, Jabril, Khairuddin Omar, Mohammad Faidzul, and Ali Mady. "Arabic Handwriting Data Base for Text Recognition." Procedia Technology 11 (2013): 580–84. http://dx.doi.org/10.1016/j.protcy.2013.12.231.

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Sternby, Jakob, Jonas Morwing, Jonas Andersson, and Christer Friberg. "On-line Arabic handwriting recognition with templates." Pattern Recognition 42, no. 12 (2009): 3278–86. http://dx.doi.org/10.1016/j.patcog.2008.12.017.

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Boulid, Youssef, Abdelghani Souhar, and Mohamed Elyoussfi Elkettani. "Multi-agent Systems for Arabic Handwriting Recognition." International Journal of Interactive Multimedia and Artificial Intelligence 4, no. 6 (2017): 31. http://dx.doi.org/10.9781/ijimai.2017.03.012.

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Ramya, Atchuta, B. Buddana V. S. S. Sri Rama Varshith, Allada Nomiya, Deva V. Naga Sai Siva Ganesh, and Balla Dinesh Babu. "DIGITDETECT: A CNN-BASED SYSTEM FOR MANUAL HANDWRITING RECOGNITION." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 91–95. https://doi.org/10.36893/jnao.2025.v16i01.011.

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The project "DigitDetect: A Handwritten Digit Recognition Using CNN" focuses on developing an advanced system for recognizing handwritten digits with high accuracy. Leveraging Convolutional Neural Networks (CNNs), the system overcomes the challenges faced by traditional methods like KNearest Neighbors (KNN), such as variations in handwriting styles, sizes, and orientations. By using the MNIST dataset comprising 28x28 grayscale images, the model undergoes data preprocessing and augmentation, ensuring robust generalization. This approach highlights the efficiency of CNNs in automatically extract

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Ahlawat, Savita, Amit Choudhary, Anand Nayyar, Saurabh Singh, and Byungun Yoon. "Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)." Sensors 20, no. 12 (2020): 3344. http://dx.doi.org/10.3390/s20123344.

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Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks

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Sarwati Rahayu, Sulis Sandiwarno, Erwin Dwika Putra, Marissa Utami, and Hadiguna Setiawan. "Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab." JSAI (Journal Scientific and Applied Informatics) 6, no. 2 (2023): 234–41. http://dx.doi.org/10.36085/jsai.v6i2.5379.

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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.

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Rastogi, Rohit, Himanshu Upadhyay, Akshit Rajan Rastogi, et al. "Knowledge Extraction in Digit Recognition Using MNIST Dataset." International Journal of Knowledge Management 17, no. 4 (2021): 52–75. http://dx.doi.org/10.4018/ijkm.2021100103.

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In handwriting recognition, traditional systems have relied heavily on handcrafted features and a massive amount of prior data and knowledge. Deep learning techniques have been the focus of research in the field of handwriting digit recognition and have achieved breakthrough performance in the last few years for knowledge extraction and management. KM and knowledge pyramid helps the project with its relationship with big data and IoT. The layers were selected randomly by which the performance of all the cases was found different. Data layers of the knowledge pyramid are formed by the sensors a

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Fergani, Khaoula, and Abdelhak Bennia. "New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method." International Journal of Engineering and Technologies 14 (September 2018): 14–30. http://dx.doi.org/10.18052/www.scipress.com/ijet.14.14.

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A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observa

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Fergani, Khaoula, and Abdelhak Bennia. "New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method." International Journal of Engineering and Technologies 14 (September 21, 2018): 14–30. http://dx.doi.org/10.56431/p-nb4392.

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A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observa

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Alwagdani, Maram Saleh, and Emad Sami Jaha. "Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination." Sensors 23, no. 15 (2023): 6774. http://dx.doi.org/10.3390/s23156774.

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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 t

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I. Abdalla, Mahmoud, Mohsen A. Rashwan, and Mohamed A. Elserafy. "Generating realistic Arabic handwriting dataset." International Journal of Engineering & Technology 8, no. 4 (2019): 460. http://dx.doi.org/10.14419/ijet.v8i4.29786.

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During the previous year's holistic approach showing satisfactory results to solve ‎the ‎problem of Arabic handwriting word recognition instead of word letters ‎‎segmentation.‎ ‎In this paper, we present an efficient system for ‎ generation realistic Arabic handwriting dataset from ASCII input ‎text. We carefully selected simple word list that contains most Arabic ‎letters normal and ligature connection cases. To improve the ‎performance of new letters reproduction we developed our ‎normalization method that adapt its clustering action according to ‎created Arabic letters families. We enhanced

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Satya Nugraha, Gibran, I. Gede Pasek Suta Wijaya, Fitri Bimantoro, Ario Yudo Husodo, and Faqih Hamami. "Arabic Character Recognition Using CNN LeNet-5." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2183. http://dx.doi.org/10.62527/joiv.7.4.2422.

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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 datas

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Tuba, Ira, Una Tuba, and Mladen Veinović. "Classification methods for handwritten digit recognition: A survey." Vojnotehnicki glasnik 71, no. 1 (2023): 113–35. http://dx.doi.org/10.5937/vojtehg71-36914.

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Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit recognition problem, from linear classifiers to convolutional neural networks. Results: Handwritten digit recognition classification accuracy tested on the MNIST dataset while using training and testing sets is now higher than 99.5% and the most successful method is a convolutional neural network. Conclusions: Handwritten digit recognition is a prob

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Impedovo, S., F. M. Mangini, and D. Barbuzzi. "A novel prototype generation technique for handwriting digit recognition." Pattern Recognition 47, no. 3 (2014): 1002–10. http://dx.doi.org/10.1016/j.patcog.2013.04.016.

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Almisreb, Ali Abd, Nooritawati Md Tahir, Sherzod Turaev, Mohammed A. Saleh, and Syed Abdul Mutalib Al Junid. "Arabic Handwriting Classification using Deep Transfer Learning Techniques." Pertanika Journal of Science and Technology 30, no. 1 (2022): 641–54. http://dx.doi.org/10.47836/pjst.30.1.35.

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Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable mode

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