Relevant bibliographies by topics / Arabic Handwriting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Arabic Handwriting'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Arabic Handwriting"

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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 (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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Arabic Handwriting"

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BOUAMAMA, SANA. "Arabic Handwriting: Cinematic and Geometric descriptors." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/20265.

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Handwriting is a fundamental skill that impacts various fields of one’s everyday-life and professional performance. It plays a crucial role due to its implications in motor and cognitive development of children’s performance in school as well as their self- esteem depends on their handwriting. Nowadays, pupils spend 30 al 60% of their time in school on handwriting and fine motor tasks. Learning to produce a legible handwriting takes a lot of time and effort even for typically developing children (Smith-Engelsman, 1995). The general purpose of the thesis was twofold. One aim was to contribute evidence on the literature that Tunisian children with handwriting difficulties will demonstrate significant difference in the legibility and kinematics of their writing movement compared to proficient writers. The main goal of the study was to determine whether these difficulties are linked somehow to the characteristic of the Arabic writing system. The specific purpose of the thesis was to investigate whether the reading habits may influence the performance of Arabic and Italian adults in a character recognition task. Relating handwriting performance to its underlying processes is important to understand normal handwriting, but also to help restore handwriting deficiencies. The data presented in the first study are extrapolated from a more general research whose purpose is to describe and compare proficient and dysgraphic handwriting in Arabic children using the paradigm proposed by Zesiger (2003). From the very beginning it was clear that the production of schoolchildren with handwriting difficulties possesses some recurrent features. Still there is not a phenomenology for Arabic handwriting to explain it. Thirty 8-10 Tunisian schoolchildren (15 poor writers and 15 proficient writers) participated to the experiment. They were asked to copy a list of words, sentences and letters and to draw 27 pictures of the TOPIG-Test. Their writing and drawing were recorded through a digitizing tablet. Results showed that 40% of participants with writing difficulties used to write letters with a wrong movement direction, omitted dots (on the top and under the letters), and were unable to draw complex figures. A possible explanation of these handwriting difficulties is an inadequate strategy of producing the movement combined to the complexity of the letter. The second study aimed to investigate the kinematics of the handwriting and analyze it using a digitizer tablet. An important purpose of this thesis is to develop a tool that permits to analyze handwriting produced from right to left and vice versa. To this end, a program was created: VB Digital Draw, which is experimental software expressly developed in our department for recording and analyzing the data of my dissertation (Toneatto et al., in progress). Twenty Tunisian children were asked to write six times in six different conditions the Arabic word Sullam [سلم] using a digitizer Wacom pen and Tablet. Results demonstrated that Poor Writers (PW) and Good Writers (GW) presented contrasting profiles in all measured descriptors. Furthermore, PW and GW differ significantly on movement velocities, dysfluency, duration, and stroke production. Comparing to GW, PW’s handwriting was smaller, slower, less fluent and discontinuous (high stroke production). To conclude, the general profile of PW that emerged in this study suggests a deficit at both motor programming and execution levels; since the irregularity of the handwriting raised in all kinematic variables. However, I did not consider that children who were identified as PW are dysgraphic but poor writers. The last study of my thesis was based on the evidence from literature which confirm my belief that writing and reading are intimately linked; An advantage for word recognition in the right visual field and in some case in the upper right visual field has been shown by Darler et al. (2004) and Hagenbeek et al. (2002). The interpretation of these visual field asymmetries is in terms of directional scanning tendencies arising from reading habits. I assume that the main process when reading is influenced by motor activity. Such process is also based on spatial competences. Nevertheless, in a previous experiment we showed that the recognition of a printed letter is primed by the coincidence between fixation point and handwriting starting point. Thus, visual filed asymmetries seems to reflect both reading and writing habits. To further explore this hypothesis I carried out an experiment on Arabic and Italian people who are characterized by opposite script directions. I investigated whether short presentations of four rotations of the character “ ” ( ) presented in upper right, upper left, lower left, and lower right visual fields provide additional information about the influence of reading and writing habits on character recognition. Thirty Italian and thirty Tunisian students of Psychology participated to the experiment. Participants were asked to recognise as quick as possible the character (“u”, “c”, “mirror u”, “mirror c”).or the direction (down, up, left, and right). Further analyses were performed on the ELP (eye landing point) which represents the (eye) fixation point with the four geometrical angles of the character. The results confirm an advantage of the upper right visual field and a facilitation of recognizing the character when the fixation point corresponds to the starting point of writing it. Besides, a difference between the Italian and the Arabic performance was found.
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Al-Hadhrami, Ahmed Abdullah Nasser. "Regional and national variation in Arabic handwriting." Thesis, University of Central Lancashire, 2013. http://clok.uclan.ac.uk/8560/.

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It has been established in a number of research publications that a careful study of general handwriting features based on class characteristics could indicate either the place or the country where the writer was first taught to write. Using these studies as the basis, this research was carried out explicitly to understand the characteristics of Arabic handwriting. The aim of this study was to determine the presence of any particular features or characteristics that may be common to individuals of a given region or nationality. This was done by obtaining samples of handwriting collected from individuals of four countries including; Jordan, Morocco, Oman and Tunisia, where Jordon and Oman are considered to be Eastern Arab world and Morocco and Tunisia in the Western Arab world. An attempt was made to establish whether it was possible to determine either the region or nationality of the writer of an Arabic passage of text, based on the formation and the style of the handwriting using specific Arabic characters. Different steps were taken towards the identification of the class characteristics of Arabic handwriting in this study starting with the collection of 600 handwriting samples from the participants in four Arabic countries employing; 150 handwriting samples from each. Ten different characters and one word were selected for examination, with more than one form of each character in different positions being identified and the handwriting samples classified accordingly. In total, 221 class characteristics were identified from the samples based on different criteria including the shape, number of strokes, pen movement and starting point. Tests of association using chi-squared on individual characters showed that the p-value is less than 0.001 in every case. Correspondence analysis was used to produce a plot of relative similarities where the different countries appear as discernible, but overlapping groups. ANOSIM showed these groups to be statistically different (R = 0.321 p = 0.0002, 1000 permutations). Tree analysis was used to create a classification system and blind tests were conducted to test the accuracy of the classification system. On the basis of the statistics used, significant differences were found in character forms used by the individuals from the four Arabic countries, suggesting that either region or nationality of the writer may potentially be predicted with a useful degree of accuracy. Though the samples were obtained from only four countries out of a total of 22 Arab countries and only ten characters and one word out of 28 characters were chosen in this study, the results obtained are valuable and useful, particularly to Forensic Document Examiners (FDEs). In turn this could be implemented in practice in a situation where a questioned document containing Arabic text is presented and the suspected author could have come from one of the four considered countries.
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Elzobi, Moftah M. [Verfasser]. "Unconstrained recognition of offline Arabic handwriting using generative and discriminative classification models / Moftah M. Elzobi." Magdeburg : Universitätsbibliothek, 2017. http://d-nb.info/1135662185/34.

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Morillot, Olivier. "Reconnaissance de textes manuscrits par modèles de Markov cachés et réseaux de neurones récurrents : application à l'écriture latine et arabe." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0002.

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La reconnaissance d’écriture manuscrite est une composante essentielle de l’analyse de document. Une tendance actuelle de ce domaine est de passer de la reconnaissance de mots isolés à celle d’une séquence de mots. Notre travail consiste donc à proposer un système de reconnaissance de lignes de texte sans segmentation explicite de la ligne en mots. Afin de construire un modèle performant, nous intervenons à plusieurs niveaux du système de reconnaissance. Tout d’abord, nous introduisons deux méthodes de prétraitement originales : un nettoyage des images de lignes de texte et une correction locale de la ligne de base. Ensuite, nous construisons un modèle de langage optimisé pour la reconnaissance de courriers manuscrits. Puis nous proposons deux systèmes de reconnaissance à l’état de l’art fondés sur les HMM (Hidden Markov Models) contextuels et les réseaux de neurones récurrents BLSTM (Bi-directional LongShort-Term Memory). Nous optimisons nos systèmes afin de proposer une comparaison de ces deux approches. Nos systèmes sont évalués sur l’écriture cursive latine et arabe et ont été soumis à deux compétitions internationales de reconnaissance d’écriture. Enfin, enperspective de notre travail, nous présentons une stratégie de reconnaissance pour certaines chaînes de caractères hors-vocabulaire
Handwriting recognition is an essential component of document analysis. One of the popular trends is to go from isolated word to word sequence recognition. Our work aims to propose a text-line recognition system without explicit word segmentation. In order to build an efficient model, we intervene at different levels of the recognition system. First of all, we introduce two new preprocessing techniques : a cleaning and a local baseline correction for text-lines. Then, a language model is built and optimized for handwritten mails. Afterwards, we propose two state-of-the-art recognition systems based on contextual HMMs (Hidden Markov Models) and recurrent neural networks BLSTM (Bi-directional Long Short-Term Memory). We optimize our systems in order to give a comparison of those two approaches. Our systems are evaluated on arabic and latin cursive handwritings and have been submitted to two international handwriting recognition competitions. At last, we introduce a strategy for some out-of-vocabulary character strings recognition, as a prospect of future work
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Ball, Gregory Raymond. "Arabic handwriting recognition using machine learning approaches." 2007. http://proquest.umi.com/pqdweb?did=1397920781&sid=6&Fmt=2&clientId=39334&RQT=309&VName=PQD.

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Thesis (Ph.D.)--State University of New York at Buffalo, 2007.
Title from PDF title page (viewed on Feb. 14, 2008) Available through UMI ProQuest Digital Dissertations. Thesis adviser: Srihari, Sargur N. Includes bibliographical references.
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Melhi, M., Stanley S. Ipson, and W. Booth. "A novel triangulation procedure for thinning hand-written text." 2001. http://hdl.handle.net/10454/3827.

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This paper describes a novel procedure for thinning binary text images by generating graphical representations of words within the image. A smoothed polygonal approximation of the boundaries of each word is first decomposed into a set of contiguous triangles. Each triangle is then classified into one of only three possible types from which a graph is generated that represents the topological features of the object. Joining graph points with straight lines generates a final polygon skeleton that, by construction, is one pixel wide and fully connected. Results of applying the procedure to thinning Arabic and English handwriting are presented. Comparisons of skeleton structure and execution time with results from alternative techniques are also presented. The procedure is considerably faster than the alternatives tested when the image resolution is greater than 600 dpi and the graphical representation often needed in subsequent recognition steps is available without further processing.
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Ahmad, Mutieah. "Naturerfaheungen bei elektronisch unterstützer Lernumgebung, unter besonderer Berücksichtigung von arabischen Kinder in Deutschland." Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-AEFA-3.

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Books on the topic "Arabic Handwriting"

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Doermann, David, and Stefan Jaeger, eds. Arabic and Chinese Handwriting Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78199-8.

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El-Sodaney, M. Basic Arabic handwriting: Nashk script : student's book. London: Sodaney Publishers, 1986.

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Wightwick, Jane. Easy Arabic script: A step-by-step guide to handwriting. New York: McGraw-Hill, 2005.

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El-Dessouki, M. Basic Arabic handwriting =: Les éléments de l'écriture arabe. London: Sodaney Publishers, 1985.

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Islamiyyah, Tarbiyah, and Sumayyah Quan. Arabic Handwriting Workbook. Independently Published, 2020.

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Faruq, Hakim Selim Imam. Arabic Handwriting for Beginners (Arabic Edition). CreateSpace Independent Publishing Platform, 2015.

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Sayegy, Lily. Arabic Handwriting Workbook II. Intl Book Centre, 1990.

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Sayegh, Lily. First Arabic Handwriting Workbook. Intl Book Centre, 1990.

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Aljilani, Abdel. Arabic Notebook: Handwriting Letters Workbook Arabic Caligraphy. Independently Published, 2020.

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publishing, arabic Troy. Arabic Writing: Blank Lines Arabic Handwriting for Beginners. Independently Published, 2020.

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Book chapters on the topic "Arabic Handwriting"

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Abu-Chacra, Faruk. "Punctuation and handwriting." In Arabic, 12–16. Second edition. | Milton Park, Abingdon, Oxon; New York : Routledge, 2018. | Series: Routledge essential grammars |: Routledge, 2017. http://dx.doi.org/10.4324/9781315620091-3.

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Märgner, Volker, and Haikal El Abed. "Arabic Handwriting Recognition Competitions." In Guide to OCR for Arabic Scripts, 395–422. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4072-6_17.

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Tamen, Zahia, Dalila Boughaci, and Habiba Drias. "Arabic Handwriting Recognition with Wavelets." In Proceedings of Sixth International Congress on Information and Communication Technology, 811–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1781-2_70.

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Belaïd, Abdel, and Christophe Choisy. "Human Reading Based Strategies for Off-Line Arabic Word Recognition." In Arabic and Chinese Handwriting Recognition, 36–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78199-8_3.

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Märgner, Volker, and Haikal El Abed. "Databases and Competitions: Strategies to Improve Arabic Recognition Systems." In Arabic and Chinese Handwriting Recognition, 82–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78199-8_6.

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Alkhoury, Ihab, Adrià Giménez, and Alfons Juan. "Arabic Handwriting Recognition Using Bernoulli HMMs." In Guide to OCR for Arabic Scripts, 255–72. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4072-6_10.

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Srihari, Sargur N., and Gregory Ball. "An Assessment of Arabic Handwriting Recognition Technology." In Guide to OCR for Arabic Scripts, 3–34. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4072-6_1.

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Ghadhban, Haitham Qutaiba, Muhaini Othman, Noor Azah Samsudin, Mohd Norasri Bin Ismail, and Mustafa Raad Hammoodi. "Survey of Offline Arabic Handwriting Word Recognition." In Advances in Intelligent Systems and Computing, 358–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36056-6_34.

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Mostafa, Mohamed A., Muhammad Al-Qurishi, and Hassan I. Mathkour. "Towards Personality Classification Through Arabic Handwriting Analysis." In Research & Innovation Forum 2019, 557–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30809-4_51.

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Kessentini, Yousri, Thierry Paquet, and AbdelMajid Ben Hamadou. "Multi-stream Markov Models for Arabic Handwriting Recognition." In Guide to OCR for Arabic Scripts, 335–50. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4072-6_14.

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Conference papers on the topic "Arabic Handwriting"

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Margner, V., and H. El Abed. "Arabic Handwriting Recognition Competition." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4377120.

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Al-Salman, AbdulMalik, and Haifa Alyahya. "Arabic online handwriting recognition." In IML 2017: International Conference on Internet of Things and Machine Learning. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3109761.3158377.

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Kherallah, Monji, Najiba Tagougui, Adel M. Alimi, Haikal El Abed, and Volker Margner. "Online Arabic Handwriting Recognition Competition." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.289.

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Chergui, Leila, Maamar Kef, and Salim Chikhi. "New hybrid Arabic handwriting recognizer." In 2012 6th International Conference on Sciences of Electronic, Technologies of Information and Telecommunications (SETIT). IEEE, 2012. http://dx.doi.org/10.1109/setit.2012.6481935.

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Srihari, Sargur N., and Gregory R. Ball. "Writer Verification of Arabic Handwriting." In 2008 The Eighth IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2008. http://dx.doi.org/10.1109/das.2008.81.

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Margner, V., M. Pechwitz, and H. E. Abed. "ICDAR 2005 Arabic handwriting recognition competition." In Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE, 2005. http://dx.doi.org/10.1109/icdar.2005.52.

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Märgner, Volker, and Haikal El Abed. "ICDAR 2009 Arabic Handwriting Recognition Competition." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.256.

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Saabni, Raid, and Jihad El-Sana. "Hierarchical On-line Arabic Handwriting Recognition." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.263.

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Margner, Volker, and Haikal El Abed. "ICDAR 2011 - Arabic Handwriting Recognition Competition." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.287.

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Aburas, Abdurazzag Ali, and Mohamed E. Gumah. "Arabic handwriting recognition: Challenges and solutions." In 2008 International Symposium on Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/itsim.2008.4631744.

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