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International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-89 April-2022
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Paper Title : Real-time feedback engine for online jawi character recognition
Author Name : Norizan Mat Diah, Ratna Zuarni Ramli, Nor Azan Mat Zin and Azizi Abdullah
Abstract :

Jawi is a type of cursive writing derived from the Arabic alphabets and adopted for writing the Malay language. The Jawi alphabet has 36 basic characters, in which 28 characters are similar to Arabic characters. Studies on online Jawi characters recognition are still minimal; most studies focus more on offline. Therefore, the online Jawi characters recognition engine has developed, involving two stages; the template modelling process and the recognition process using matching templates. The recognition engine developed can provide real-time feedback on the accuracy of Jawi characters writing activities by users. The recognition engine’s feedback accuracy assessed using a comparative analysis method by looking at the agreement score between recognition engine feedback and experts’ feedback. The Krippendorff’s Alpha Reliability Coefficient Index (Krippendorff’s ) agreement score was used to measure the agreement between the recognition engine feedback and experts’ feedback. Krippendorff’s agreement score assessment results found that the accuracy of recognition engine feedback was almost the same as the experts’ feedback. Therefore, it can conclude that the recognition engine developed has high accuracy and can be used to recognise Jawi characters online.

Keywords : Online, Jawi characters, Recognition engine, Feedback, Agreement score.
Cite this article : Diah NM, Ramli RZ, Zin NA, Abdullah A. Real-time feedback engine for online jawi character recognition. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(89):477-489. DOI:10.19101/IJATEE.2021.874758.
References :
[1]Zamri AA, Mazmuzidin MZ. Mengenali Jawi: the effectiveness of augmented reality application for jawi learning. Journal of Computing Technologies and Creative Content. 2021; 6(2):67-71.
[Google Scholar]
[2]Abdullah F, Ab RAH, Ali AM, Kadir FK, Adam F. Jawi script and the Malay society: historical background and development. International Journal of Management. 2020; 11(7).
[Google Scholar]
[3]Nahar N, Safar J. Pengajaran Jawi berkesan dalam usaha memartabatkan warisan budaya bangsa. In proceedings of the international conference on education towards global Peace 2016 (pp.1-17).
[Google Scholar]
[4]Ab GMA. Improving the writing skills of Jawi connection letters of students with learning disabilities using the finger step. Turkish Journal of Computer and Mathematics Education. 2021; 12(11):5307-12.
[Google Scholar]
[5]Ibrahim R, Embong R, Kadir FK, Hashim HA. Empowerment of jawi writing then and now. BITARA International Journal of Civilizational Studies and Human Sciences. 2019; 2(1):29-37.
[Google Scholar]
[6]James KH, Engelhardt L. The effects of handwriting experience on functional brain development in pre-literate children. Trends in Neuroscience and Education. 2012; 1(1):32-42.
[Crossref] [Google Scholar]
[7]Amin MM, Badioze ZH, Ahmad A. Preliminary study on haptic approach in learning Jawi handwriting skills. In international visual informatics conference 2011 (pp. 168-79). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[8]Amin MM, Zaman HB, Ahmad A. Developing architecture of haptik visual Jawi (HV-Jawi). In international visual informatics conference 2013 (pp. 729-41). Springer, Cham.
[Crossref] [Google Scholar]
[9]Nasrudin MF, Omar K, Zakaria MS, Yeun LC. Handwritten cursive Jawi character recognition: a survey. In fifth international conference on computer graphics, imaging and visualisation 2008 (pp. 247-56). IEEE.
[Crossref] [Google Scholar]
[10]Razak Z, Zulkiflee K, Noor NM, Salleh R, Yaacob M. Off-line handwritten Jawi character segmentation using histogram normalization and sliding window approach for hardware implementation. Malaysian Journal of Computer Science. 2009; 22(1):34-43.
[Crossref] [Google Scholar]
[11]Redika R, Omar K, Nasrudin MF. Handwritten Jawi words recognition using hidden Markov models. In international symposium on information technology 2008 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[12]Heryanto A, Nasrudin MF, Omar K. Offline Jawi handwritten recognizer using hybrid artificial neural networks and dynamic programming. In international symposium on information technology 2008 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[13]Razak Z, Ghani NA, Tamil EM, Idris MYI, Noor NM, Yusoff MYM. Chip design for jawi features extraction and recognition using discrete wavelet transform and K-mismatch algorithm. International Colloquium on Signal Processing and its Application (CSPA) 2008, Kuala Lumpur, Malaysia,
[14]Nasrudin MF, Petrou M. Offline handwritten jawi recognition using the trace transform. In international conference on pattern analysis and intelligence robotics 2011 (pp. 87-91). IEEE.
[Crossref] [Google Scholar]
[15]Arnia F, Saddami K, Munadi K. Moment invariant-based features for Jawi character recognition. International Journal of Electrical & Computer Engineering. 2019; 9(3).
[Crossref] [Google Scholar]
[16]Hasan AH., Omar K, Nasrudin MF. Trace transform feature learning for offline Jawi handwritten recognition. International Journal on Advanced Science, Engineering and Information Technology; 2021; 11:37-42.
[Crossref]
[17]Ahmad NA. Online non-cursive JAWI character recognition system (Doctoral dissertation).
[Google Scholar]
[18]Saddami K, Munadi K, Muchallil S, Arnia F. Improved thresholding method for enhancing Jawi binarization performance. In IAPR international conference on document analysis and recognition 2017 (pp. 1108-13). IEEE.
[Crossref] [Google Scholar]
[19]Muchallil S, Nazaruddin. Online application of printed Jawi character recognition. Indian Journal of Science and Technology. 2017; 10(12): 1-5.
[Crossref]
[20]Rahim SN, Hamzah R. E-Jawi: digital learning tool for jawi character recognition. Journal of Computing Technologies and Creative Content. 2016; 1(1):55-8.
[Google Scholar]
[21]Diah NM, Mat ZNA. Digital training tool framework for Jawi character formation. In international visual informatics conference 2011(pp. 164-75). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[22]Fatahillah D. Transliteration of Malay to jawi using character mapping techniques. Repositor Journal. 2020; 2(1):9-14.
[Crossref] [Google Scholar]
[23]Althobaiti H, Lu C. A survey on Arabic optical character recognition and an isolated handwritten arabic character recognition algorithm using encoded freeman chain code. In conference on information sciences and systems 2017 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[24]Dinges L, Al-hamadi A, Elzobi M, El-etriby S. Synthesis of common Arabic handwritings to aid optical character recognition research. Sensors. 2016; 16(3):1-25.
[Crossref] [Google Scholar]
[25]Elanwar RI, Rashwan MA, Mashali SA. Simultaneous segmentation and recognition of Arabic characters in an unconstrained on-line cursive handwritten document. International Journal of Computer and Information Science and Engineering. 2007; 4:203-6.
[Google Scholar]
[26]Lawal IA, Abdel-aal RE, Mahmoud SA. Recognition of handwritten Arabic (Indian) numerals using freemans chain codes and abductive network classifiers. In international conference on pattern recognition 2010 (pp. 1884-7). IEEE.
[Crossref] [Google Scholar]
[27]Ali M. Freeman chain code contour processing for handwritten isolated Arabic characters recognition. Available at SSRN 3201487. 2012.
[Crossref] [Google Scholar]
[28]Darmawan J, Wijaya AH, Hakim L, Tannady H. Comparing freeman chain code 4 adjacency algorithm and LZMA algorithm in binary image compression. In Journal of Physics: Conference Series 2021(p. 012045). IOP Publishing.
[Google Scholar]
[29]Afrianto I, Riyanda R, Atin S. Implementation of freeman chain code algorithm and support vector machine (SVM) on identification of Malay Arabic script. In seminar national information technology application 2018.
[Google Scholar]
[30]Kadi M. A modified algorithm for improving the coding of Arabic characters by the Freeman chain code. In international conference on wireless technologies, embedded and intelligent systems 2019 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[31]Sternby J, Morwing J, Andersson J, Friberg C. On-line Arabic handwriting recognition with templates. Pattern Recognition. 2009; 42(12):3278-86.
[Crossref] [Google Scholar]
[32]Omer MA, Ma SL. Online Arabic handwriting character recognition using matching algorithm. In international conference on computer and automation engineering (ICCAE) 2010 (pp. 259-62). IEEE.
[Google Scholar]
[33]Abdalkafor AS, Alheeti KM, Al-jobouri L. A feature extraction method for Arabic offline handwritten recognition system using naïve Bayes classifier. In international conference on computing and communications applications and technologies 2021 (pp. 82-7). IEEE.
[Crossref] [Google Scholar]
[34]Lamghari N, Charaf ME, Raghay S. Template matching for recognition of handwritten Arabic characters using structural characteristics and Freeman code. The International Journal of Computer Science and Information Security. 2016; 14(12):31-40.
[Google Scholar]
[35]Santosh KC, Nattee C. A comprehensive survey on on-line handwriting recognition technology and its real application to the Nepalese natural handwriting. Kathmandu University Journal of Science, Engineering, and Technology. 2009; 5(I):31-55.
[Google Scholar]
[36]Gonzalez RC. Digital image processing. Pearson Education India; 2009.
[Google Scholar]
[37]Annapurna P, Kothuri S, Lukka S. Digit recognition using freeman chain code. International Journal of Application or Innovation in Engineering and Management (IJAIEM). 2013; 2(8):362-5.
[Google Scholar]
[38]http://www.regextester.com/. Accessed 12 November 2020.
[39]Ahmad AT, Maen H. Recognition of on-line handwritten Arabic digits using structural features and transition network. Informatica. 2008; 32(3).
[Google Scholar]
[40]Biadsy F, El-sana J, Habash NY. Online Arabic handwriting recognition using hidden Markov models. Proceedings of the 10th international workshop on frontiers of handwriting and recognition. 2006.
[Google Scholar]
[41]Shu H. On-line handwriting recognition using hidden Markov models (Doctoral dissertation, Massachusetts Institute of Technology).1997.
[Google Scholar]
[42]Catmull E, Rom R. A class of local interpolating splines. In computer aided geometric design 1974 (pp. 317-26). Academic Press.
[Crossref] [Google Scholar]
[43]Eyiyurekli M, Grimm C, Breen D. Editing level-set models with sketched curves. In proceedings of the EUROGRAPHICS symposium on sketch-based interfaces and modeling 2009 (pp. 45-52).
[Crossref] [Google Scholar]
[44]Vatavu RD. Real-time acquisition of human gestures for interacting with virtual environments (Doctoral dissertation, Lille 1).
[Google Scholar]
[45]Wang Y, Shen D, Teoh EK. Lane detection using spline model. Pattern Recognition Letters. 2000; 21(8):677-89.
[Crossref] [Google Scholar]
[46]Zitouni R, Bezine H, Arous N. Online handwritten Arabic scripts recognition using stroke-based class labeling scheme. International Journal of Computational Intelligence Systems. 2021; 14(1):187-98.
[Crossref] [Google Scholar]
[47]Gupta K, Rao SV, Viswanath P. Speeding up online character recognition. In proceedings of image and vision computing New Zealand 2007 (pp.41-5).
[Google Scholar]
[48]Krippendorff K. Towards a radically social constructivism. Departmental Papers (ASC). 2008; 3(2):91-4.
[Google Scholar]
[49]http://repository.upenn.edu/asc_papers/43. Accessed 1 November 2020.
[50]Krippendorff K. Content analysis: an introduction to its methodology. Sage Publications; 2018.
[Google Scholar]
[51]Taylor J, Watkinson D. Indexing reliability for condition survey data. The Conservator. 2007; 30(1):49-62.
[Crossref] [Google Scholar]
[52]http://matthewlombard.com/reliability/index_print.html. Accessed 20 November 2019.
[53]Lombard M, Snyder‐duch J, Bracken CC. Content analysis in mass communication: assessment and reporting of intercoder reliability. Human Communication Research. 2002; 28(4):587-604.
[Crossref] [Google Scholar]
[54]https://gs.statcounter.com/os-market-share/mobile/malaysia. Accessed 26 September 2021.
[55]https://machinelearningmastery.com/types-of-classification-in-machine-learning/. Accessed 21 November 2021.