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Journal articles on the topic 'Arabic Handwriting'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

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 (January 29, 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 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.

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2

Youssef, Nahla Ibrahim, and Nadia Abd-Alsabour. "A REVIEW ON ARABIC HANDWRITING RECOGNITION." Journal of Southwest Jiaotong University 57, no. 6 (December 30, 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 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.

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3

I. Abdalla, Mahmoud, Mohsen A. Rashwan, and Mohamed A. Elserafy. "Generating realistic Arabic handwriting dataset." International Journal of Engineering & Technology 8, no. 4 (October 19, 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 Gaussian Mixture ‎Model process to learn letters template by detecting the ‎number and position of Gaussian component by implementing ‎Ramer-Douglas-Peucker‎ algorithm which improve the new letters ‎shapes reproduced by using and Gaussian Mixture Regression. ‎‎We learn the translation distance between word-part to achieve ‎real handwriting word generation shape.‎ Using combination of LSTM and CTC layer as a recognizer to validate the ‎efficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.‎

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4

Salameh-Matar, Abeer, Naser Basal, and Naomi Weintraub. "Relationship between body functions and Arabic handwriting performance at different acquisition stages." Canadian Journal of Occupational Therapy 85, no. 5 (December 2018): 418–27. http://dx.doi.org/10.1177/0008417419826114.

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Background. The written languages and handwriting acquisition stages place different demands on the writer. Therefore, the relationship between body functions and handwriting performance may vary in different languages and acquisition stages; yet these demands have not been studied in the Arabic language. Purpose. We examined the relationship between linguistic, visual-motor integration (VMI), and motor coordination (MC) functions and Arabic handwriting at two handwriting acquisition stages. Method. This study used a cross-sectional and correlative design. Second- ( n = 54) and fourth-grade ( n = 59) students performed tasks examining reading, handwriting automaticity, VMI, MC, and copying a text. Findings. Handwriting automaticity significantly explained the variance in handwriting speed in both grades, in addition to the VMI in second grade and the MC in fourth grade. Enhanced performance in the VMI increased the likelihood of having good legibility in second but not in fourth grade. Implications. Similar to other languages, the body functions related to Arabic handwriting vary at the different acquisition stages. Handwriting evaluation should be adjusted to students’ acquisition stage.

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5

Beldjehem, Mokhtar. "A Granular Framework for Recognition of Arabic Handwriting." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 5 (September 20, 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 and a high granularity level that consists on morphological analysis dedicated to segmentation/recognition. The original idea is the effective use of Arabic word's morphology in the recognition not only in post-processing. This architecture carries well around the Arabic word's morphology, as typically in Arabic, the Arabic word's morphology is by excellence the logical structure (even semantic) of a given Arabic word, whereas the visual data constitute the physical geometric (topological) structure of a given word. We need to integrate both of them for an effective cooperative recognition of Arabic Handwriting. This framework subsumes the lexicon-driven approaches; in that it can recognize a word that does not exist within the lexicon.

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6

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 (January 10, 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 model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.

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7

Al-Helali, Baligh M., and Sabri A. Mahmoud. "Arabic Online Handwriting Recognition (AOHR)." ACM Computing Surveys 50, no. 3 (October 9, 2017): 1–35. http://dx.doi.org/10.1145/3060620.

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8

Elarian, Yousef, Irfan Ahmad, Sameh Awaida, Wasfi G. Al-Khatib, and Abdelmalek Zidouri. "An Arabic handwriting synthesis system." Pattern Recognition 48, no. 3 (March 2015): 849–61. http://dx.doi.org/10.1016/j.patcog.2014.09.013.

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9

Al-Maadeed, Somaya. "Text-Dependent Writer Identification for Arabic Handwriting." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/794106.

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This paper proposes a system for text-dependent writer identification based on Arabic handwriting. First, a database of words was assembled and used as a test base. Next, features vectors were extracted from writers' word images. Prior to the feature extraction process, normalization operations were applied to the word or text line under analysis. In this work, we studied the feature extraction and recognition operations of Arabic text on the identification rate of writers. Because there is no well-known database containing Arabic handwritten words for researchers to test, we have built a new database of offline Arabic handwriting text to be used by the writer identification research community. The database of Arabic handwritten words collected from 100 writers is intended to provide training and testing sets for Arabic writer identification research. We evaluated the performance of edge-based directional probability distributions as features, among other characteristics, in Arabic writer identification. Results suggest that longer Arabic words and phrases have higher impact on writer identification.

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10

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 (November 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 current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

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11

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 (April 21, 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 extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.

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Mohammed, Mamoun Jassim, Suphian Mohammed Tariq, and Hayder Ayad. "Isolated Arabic handwritten words recognition using EHD and HOG methods." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 801. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp801-808.

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<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

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13

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

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14

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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15

El Abed, Haikal, and Volker Märgner. "ICDAR 2009-Arabic handwriting recognition competition." International Journal on Document Analysis and Recognition (IJDAR) 14, no. 1 (April 24, 2010): 3–13. http://dx.doi.org/10.1007/s10032-010-0117-5.

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16

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 (July 9, 2010): 15–23. http://dx.doi.org/10.1007/s10032-010-0124-6.

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17

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 (May 25, 2012): 209–26. http://dx.doi.org/10.1007/s10032-012-0186-8.

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18

Al-Barhamtoshy, Hassanin, Sherif Abdou, and Fakhraddin A. Al-Wajih. "A Toolkit for Teaching Arabic Handwriting." International Journal of Computer Applications 49, no. 23 (July 31, 2012): 17–23. http://dx.doi.org/10.5120/7943-1273.

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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 (October 27, 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 researcher proposed a method of new features extraction to recognize Arabic handwriting. The features extraction was done by grabbing the value of similar features among various types of font writing, to be used as a new feature of the font. Then, City Block was used to compare the obtained feature to other features of the sample for classification. The Average accuracy value obtained in this study was up to 82%.

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20

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 (October 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, 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>

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21

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 (August 1, 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 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.

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22

Aljuaid, Hanan, Dzulkifli Mohamad, and Muhammad Sarfraz. "Evaluation Approach of Arabic Character Recognition." International Journal of Computer Vision and Image Processing 1, no. 2 (April 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 recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.

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23

Khayyat, Manal, and Lamiaa Elrefaei. "A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style." International Arab Journal of Information Technology 17, no. 5 (September 1, 2020): 702–12. http://dx.doi.org/10.34028/iajit/17/5/3.

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With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al-Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score

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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 (September 30, 2016): 51–59. http://dx.doi.org/10.5121/ijaia.2016.7504.

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Boubaker, Houcine, Najiba Tagougui, Haikal El Abed, Monji Kherallah, and Adel M. Alimi. "Graphemes Segmentation for Arabic Online Handwriting Modeling." Journal of Information Processing Systems 10, no. 4 (December 31, 2014): 503–22. http://dx.doi.org/10.3745/jips.02.0006.

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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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29

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

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30

Al-Hadhrami, Ahmed A. N., Mike Allen, Colin Moffatt, and Allison E. Jones. "National characteristics and variation in Arabic handwriting." Forensic Science International 247 (February 2015): 89–96. http://dx.doi.org/10.1016/j.forsciint.2014.12.004.

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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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Raja Mohd Yazit, Raja Nur Syaheeza, Eliana Mohd Husini, Mohd Khedzir Khamis, Megat Faridrullah Zolkefli, and Yakubu Aminu Dodo. "Illuminance Level Measurement at Lower Working Plane Height in Islamic Religious School." Asian Journal of University Education 16, no. 3 (October 20, 2020): 125. http://dx.doi.org/10.24191/ajue.v16i3.11076.

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Islamic religious school is an institution that integrates Quran hafazan (memorization) in the curriculum. Between 2011 to 2017, estimated that 900 new Islamic religious schools were established in Malaysia due to high demands. Designing a classroom layout that receives sufficient daylight is important because it influences the students’ task performance such as reading and writing. The standards recommend that any classrooms require an illuminance level between 300 lx to 500 lx when measured at working plane height between 800mm to 900mm, although the working plane height of rehal used for hafazan is between 250mm to 300mm. This study focused on the illuminance level measured at rehal working plane height for Arabic handwriting as a hafazan learning task in two selected standardised classrooms at Kolej Genius Insan. The students were required to rewrite the modified Balsam Alabdulkader-Leat (BAL) Arabic eye chart, where the students’ Arabic handwriting performance were evaluated based on their word per minute (wpm) scores. Both classrooms’ average illuminance level were 507 lx to 603 lx, which were too high based on standards and guidelines. The average Arabic handwriting scores for both classrooms were 9.4 and 12.6 wpm, which shows that the inefficient average illuminance level has caused the students’ performance to be very low. It can be concluded that the existing standardised classroom layout design was not suitable for hafazan learning tasks at rehal working plane height. Thus, the classroom layout design for Islamic religious schools needed further studies, which implicated the unsatisfied built environment of the classrooms and the school education for Islamic religious schools in Malaysia. Keywords: Arabic handwriting, Daylighting, Illuminance level, rehal, working plane

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Ullah, Zahid, and Mona Jamjoom. "An intelligent approach for Arabic handwritten letter recognition using convolutional neural network." PeerJ Computer Science 8 (May 27, 2022): e995. http://dx.doi.org/10.7717/peerj-cs.995.

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Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature.

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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 observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network.Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com

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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 observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network. Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com

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Amakrane, Meryem, Ghizlane Khaissidi, Mostafa Mrabti, Alae Ammour, Belahsen Faouzi, and Ghita Aboulem. "Feature Selection of Arabic Online Handwriting Using Recursive Feature Elimination for Parkinson’s Disease Diagnosis." E3S Web of Conferences 351 (2022): 01044. http://dx.doi.org/10.1051/e3sconf/202235101044.

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Parkinson’s disease (PD) is one of the most common neurodegenerative diseases affecting a large population worldwide. Parkinson’s disease is characterized by rigidity, slowness of movement and tremors at rest, these syndromes are frequently manifested in the deterioration of handwriting. The aim of this article is to perform online Arabic handwriting analysis for two types of tasks, TASK 1: copying arabic imposed text and TASK 2: writing arabic desired text. A novel method of handwriting selection features is proposed to obtain the relevant features to efficiently identify subjects with PD, based on Recursive Feature Elimination with Cross-Validation (RFECV), three different RFE estimators were compared: Support Vector Machine, Decision Trees and Random Forest, the selected features have been fed to the same classifiers above to determine the best classifier for predicting Parkinson’s disease. Result: An accuracy of 94.4% was obtained using SVM with Linear kernel, based on 55 features selected using RFE-SVM(Linear) for TASK 1, for TASK 2 an accuracy of 93.7% was obtained using SVM with RBF kernel, based only in 7 features selected using RFE-SVM(Linear). For all the classifiers used, this technique experimentally demonstrates an increase in performance metrics.

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Albattah, Waleed, and Saleh Albahli. "Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures." Applied Sciences 12, no. 19 (October 10, 2022): 10155. http://dx.doi.org/10.3390/app121910155.

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Handwritten character recognition is a computer-vision-system problem that is still critical and challenging in many computer-vision tasks. With the increased interest in handwriting recognition as well as the developments in machine-learning and deep-learning algorithms, researchers have made significant improvements and advances in developing English-handwriting-recognition methodologies; however, Arabic handwriting recognition has not yet received enough interest. In this work, several deep-learning and hybrid models were created. The methodology of the current study took advantage of machine learning in classification and deep learning in feature extraction to create hybrid models. Among the standalone deep-learning models trained on the two datasets used in the experiments performed, the best results were obtained with the transfer-learning model on the MNIST dataset, with 0.9967 accuracy achieved. The results for the hybrid models using the MNIST dataset were good, with accuracy measures exceeding 0.9 for all the hybrid models; however, the results for the hybrid models using the Arabic character dataset were inferior.

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Bezine, Hala, and Adel M. Alimi. "Development of an Arabic Handwriting Learning Educational System." International Journal of Software Engineering & Applications 4, no. 2 (March 31, 2013): 33–49. http://dx.doi.org/10.5121/ijsea.2013.4203.

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Bensefia, Ameur. "Extraction of Arabic Handwriting Fields by Forms Matching." Journal of Signal and Information Processing 06, no. 01 (2015): 1–8. http://dx.doi.org/10.4236/jsip.2015.61001.

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Alabodi, Jafaar, and Xue Li. "An Effective Approach to Offline Arabic Handwriting Recognition." International Journal of Artificial Intelligence & Applications 4, no. 6 (November 30, 2013): 1–16. http://dx.doi.org/10.5121/ijaia.2013.4601.

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Musa Yaagoup, Khalid Mohammed, and Mohamed Elhafiz Mustafa. "Online Arabic Handwriting Characters Recognition using Deep Learning." IJARCCE 9, no. 10 (October 30, 2020): 83–92. http://dx.doi.org/10.17148/ijarcce.2020.91014.

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Almuallim, Hussein, and Shoichiro Yamaguchi. "A Method of Recognition of Arabic Cursive Handwriting." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 5 (September 1987): 715–22. http://dx.doi.org/10.1109/tpami.1987.4767970.

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Haraty, R. A., and H. M. El-Zabadani. "Abjad Hawwaz: An Offline Arabic Handwriting Recognition System." International Journal of Computers and Applications 27, no. 3 (January 2005): 178–89. http://dx.doi.org/10.1080/1206212x.2005.11441767.

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Khémiri, Akram, Afef Kacem Echi, and Mourad Elloumi. "Bayesian Versus Convolutional Networks for Arabic Handwriting Recognition." Arabian Journal for Science and Engineering 44, no. 11 (May 28, 2019): 9301–19. http://dx.doi.org/10.1007/s13369-019-03939-y.

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Al Abodi, Jafaar, and Xue Li. "An effective approach to offline Arabic handwriting recognition." Computers & Electrical Engineering 40, no. 6 (August 2014): 1883–901. http://dx.doi.org/10.1016/j.compeleceng.2014.04.014.

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Zaiz, Faouzi, Mohamed Chaouki Babahenini, and Abdelhamid Djeffal. "Puzzle based system for improving Arabic handwriting recognition." Engineering Applications of Artificial Intelligence 56 (November 2016): 222–29. http://dx.doi.org/10.1016/j.engappai.2016.09.005.

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Romeo-Pakker, Katerin, Abderrahim Ameur, Christian Olivier, and Yves Lecourtier. "Structural analysis of Arabic handwriting: segmentation and recognition." Machine Vision and Applications 8, no. 4 (July 1995): 232–40. http://dx.doi.org/10.1007/bf01219591.

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Mahmoud, Imene, Ines Chihi, Afef Abedlkrim, and Mohamed Benrejeb. "Manuscript shapes generated by novel bi-axis control algorithm based on a mathematical handwriting model." IAES International Journal of Robotics and Automation (IJRA) 4, no. 3 (September 1, 2016): 230. http://dx.doi.org/10.11591/ijra.v4i3.pp230-242.

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<span lang="EN-US">Handwriting movement is one of the most complex activities of human motions. It’s a blend of kinesthetic, cognitive, perceptual and motor components. The study of this biological process shows that bell-shaped velocity profiles are generally observed in the handwriting motion. In this paper, an identification technique, based on Recursive Least Square algorithm (RLS), is proposed to identify the pen-tip movement in human handwriting process, by using input and output data which present EMG signals and velocities according to x and y coordinates. Using the estimated coordinates that have resulted from the velocity model; we propose a novel algorithm to generate handwritten graphic traces, which is inspired from the idea of tracing circles by Bresenham bi-axis control algorithm. The effectiveness of this approach should be observed on predicting cursive Arabic letters and Arabic word written on (</span><em><span lang="EN-US">x,y</span></em><span lang="EN-US">) plane, these shapes constituting a recorded experimental basis.</span>

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AL-Saffar, Ahmed, Suryanti Awang, Wafaa AL-Saiagh, Sabrina Tiun, and A. S. Al-khaleefa. "Deep Learning Algorithms for Arabic Handwriting Recognition: A Review." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 344. http://dx.doi.org/10.14419/ijet.v7i3.20.19271.

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Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.

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Zakaria, Aya, Manal Salah, and Mostafa S. Ali. "Correlation between Development of Handwriting Skills and Cognitive Abilities in Primary School Children." NeuroQuantology 20, no. 4 (April 30, 2022): 544–51. http://dx.doi.org/10.14704/nq.2022.20.4.nq22335.

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Background: Handwriting skills are the outcome of a multiplex integration of sensorimotor, cognitive, perceptual, visual, and sensory systems cooperating to complete a successful writing activity. Objective: To detect the relationship between the development of cognitive abilities and handwriting skills in primary school children. Methods: one hundred and fifty volunteer children with ages ranging from nine to eleven years, taught in the Arabic language as a primary language, right-handed children not suffering from any musculoskeletal problems or physical disabilities, with normal visual acuity even with eyeglasses participated in this study. Handwriting legibility was assessed using the handwriting legibility scale, and cognitive abilities such as verbal comprehension, perceptual reasoning, working memory, and processing speed were assessed using the Wechsler scale. Results: The results revealed that there was a weak positive non-significant correlation between handwriting skills and cognitive abilities (perceptual reasoning, verbal comprehension, working memory, and total score) while there was a weak negative non-significant correlation between handwriting skills and working processing speed. Conclusion: according to the current research results, there is a weak positive non-significant correlation between handwriting skills and the development of cognitive abilities in primary school children.

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