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(searched for: doi:10.13176/11.217)
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, , Esraa Khaled Youssef, Asmaa Elsayed Elsayed, Reem Adel Samak, Mohammed Samy Abdelhaleem, Mohammed Mosa Tolba, Mahmoud Ragab Shehata, Mahmoud Refa’At Mahmoud, Mariam Mahmoud Abdelhameed, et al.
Multimedia Tools and Applications pp 1-37; https://doi.org/10.1007/s11042-021-11185-4

The publisher has not yet granted permission to display this abstract.
Machine Learning with Applications, Volume 5; https://doi.org/10.1016/j.mlwa.2021.100037

Abstract:
The recognition of online handwriting is a vital application of pattern recognition, which involves the extraction of spatial and temporal information of handwritten patterns, and understanding the handwritten text while writing on the digital surface. Although, online handwriting recognition is a mature but exciting and fast developing field of pattern recognition, the same is not true for many of the Indic scripts. Gurmukhi is one of such popular scripts of India, and online handwriting recognition issues for larger units as words or sentences largely remained unexplored for this script till date. The existing study and first ever attempt for online handwritten Gurmukhi word recognition has relied upon the widely used hidden Markov model. This existing study evaluated against and performed very well in their chosen metrics. But, the available online handwritten Gurmukhi word recognition system could not obtain more than 90% recognition accuracy in data dependent environment too. The present study provided benchmark results for online handwritten Gurmukhi word recognition using deep learning architecture convolutional neural network, and obtained above 97% recognition accuracy in data dependent mode of handwriting. The previous Gurmukhi word recognition system followed the stroke based class labeling approach, whereas the present study has followed the word based class labeling approach. Present Online handwritten Gurmukhi word recognition results are quite satisfactory. Moreover, the proposed architecture can be used to improve the benchmark results of online handwriting recognition of several major Indian scripts. Experimental results demonstrated that the deep learning system achieved great results in Gurmukhi script and outperforms existing results in the literature.
Artificial Intelligence - Emerging Trends and Applications; https://doi.org/10.5772/intechopen.76944

Abstract:
Handheld devices are flooding the market, and their use is becoming essential among people. Hence, the need for fast and accurate character recognition methods that ease the data entry process for users arises. There are many methods developed for handwriting character recognition especially for Latin-based languages. On the other hand, character recognition methods for Arabic language are lacking and rare. The Arabic language has many traits that differentiate it from other languages: first, the writing process is from right to left; second, the letter changes shape according to the position in the work; and third, the writing is cursive. Such traits compel to produce a special character recognition method that helps in producing applications for Arabic language. This research proposes a deterministic algorithm that recognizes Arabic alphabet letters. The algorithm is based on four categorizations of Arabic alphabet letters. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose.
Baligh M. Al-Helali, Sabri A. Mahmoud
ACM Computing Surveys, Volume 50, pp 1-35; https://doi.org/10.1145/3060620

Abstract:
This article comprehensively surveys Arabic Online Handwriting Recognition (AOHR). We address the challenges posed by online handwriting recognition, including ligatures, dots and diacritic problems, online/offline touching of text, and geometric variations. Then we present a general model of an AOHR system that incorporates the different phases of an AOHR system. We summarize the main AOHR databases and identify their uses and limitations. Preprocessing techniques that are used in AOHR, viz. normalization, smoothing, de-hooking, baseline identification, and delayed stroke processing, are presented with illustrative examples. We discuss different techniques for Arabic online handwriting segmentation at the character and morpheme levels and identify their limitations. Feature extraction techniques that are used in AOHR are discussed and their challenges identified. We address the classification techniques of non-cursive (characters and digits) and cursive Arabic online handwriting and analyze their applications. We discuss different classification techniques, viz. structural approaches, Support Vector Machine (SVM), Fuzzy SVM, Neural Networks, Hidden Markov Model, Genetic algorithms, decision trees, and rule-based systems, and analyze their performance. Post-processing techniques are also discussed. Several tables that summarize the surveyed publications are provided for ease of reference and comparison. We summarize the current limitations and difficulties of AOHR and future directions of research.
Ahmed. T. Sahlol, , Esraa Elhariri, Aboul Ella Hassanien
2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) pp 1-7; https://doi.org/10.1109/itcosp.2017.8303068

Abstract:
Although the extensive work towards building Optical Character Recognition systems(OCR) for Arabic handwritten characters, the unlimited variation and different writing styles of each character make building such these systems a big research challenge. In Arabic alphabetic system, each character has different forms (three or four) depending on its position in a word. In this paper, a handwritten character recognition system was proposed. The proposed system is implemented using a set of well-known optimizers, Bat Algorithm (BAT), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimization (GWO) algorithm. The proposed system was tested by well-known classifiers to test the efficiency; linear discriminant analysis, support vector machines and random forest. Among all of them, GWO greatly improves the classification accuracy and time efficiency. Compared to the state-of-the-art methods, the optimized feature sets were efficient than the whole feature set in terms of accuracy as well as time consumption.
Ahmed.T. Sahlol, Aboul Ella Hassanien
Handbook of Research on Natural Computing for Optimization Problems pp 897-914; https://doi.org/10.4018/978-1-5225-2229-4.ch039

Abstract:
There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.
Ahmed. T. Sahlol, Ching Y. Suen, , ,
2016 IEEE Congress on Evolutionary Computation (CEC) pp 1749-1756; https://doi.org/10.1109/cec.2016.7744000

Abstract:
There are many difficulties facing a handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of distinct character shapes, interconnections of neighboring characters and their position in the word. This paper presents a handwritten Arabic character recognition system based on BA algorithm. BA algorithm is adopted to reduce the feature set size and to improve the accuracy rate. The proposed system is trained and tested by four well-known classifiers; Bayes Network (BN), artificial neural network (ANN), K-nearest neighbors (KNN), and Random forest (RF) with CENPARMI dataset. The proposed optimization algorithm obtained promising results in terms of classification accuracy as the proposed system is able to recognize 91.59 % of our test set correctly, as well as in terms of computational time reduction. BA algorithm is more efficient in most experiments when comparing with GA and PSO. When compared our results with other related works we find that our result is the highest among other published results.
Baligh M. Al-Helali,
Published: 5 August 2016
Cybernetics and Systems, Volume 47, pp 478-498; https://doi.org/10.1080/01969722.2016.1206768

Abstract:
The widely-used PDAs, touch screens, tablet-PCs are alternatives to keyboards with the advantages of being more friendly, easy, and natural. A framework for Arabic online character recognition is developed. The framework integrates the different phases of online Arabic text recognition. The used data poses several challenges such as delayed strokes handling, connectivity problems, variability, and style change of text. We process the delayed strokes at the different phases differently to improve the overall performance. This work includes feature extraction of many features, including several novel statistical features. Experimental results on challenging online Arabic characters show encouraging results.
, , Abed Al Raoof' Bsoul
2014 3rd International Conference on User Science and Engineering (i-USEr) pp 237-241; https://doi.org/10.1109/iuser.2014.7002709

Abstract:
A very small percentage of books are available in accessible format for people with print disabilities (e.g., people with visual impairment and people with learning disabilities). The authors and publishers of e-books usually do not include certain features to make the e-book accessible. Digital talking books are electronic documents encoded in DAISY format. The purpose of this format is to provide access-for-all to digital information. This paper presents a framework for semiautomatic building of DAISY digital talking books for the Arabic language. The presented framework include: image-to-text converter, context-injector, text-to-audio-generator, and DAISY generator.
George Kour, Raid Saabne
2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR) pp 312-318; https://doi.org/10.1109/socpar.2014.7008025

Abstract:
Delaying the analysis launch until the completion of the handwritten word scribing, restricts on-line recognition systems to meet the highly responsiveness demands expected from such applications, and prevents implementing advanced features of input typing such as automatic word completion and real-time automatic spelling. This paper proposes an efficient Arabic handwritten characters recognizer aimed at facilitating real-time handwritten script analysis tasks. The fast classification is enabled by employing an efficient embedding of the feature vectors into a normed wavelet coefficients domain in which the Earth Movers Distance metric is approximated using the Manhattan distance. A sub-linear time character classification is achieved by utilizing metric indexing techniques. Using the results of the top ranked shapes of each predicted character, a list of candidate shapes of Arabic word parts is generated in a filter and refine approach to enable fast yet accurate recognition results in a dictionary-free environment. The system was trained and tested on characters and word parts extracted from the ADAB database, and promising accuracy and performance results were achieved.
Askar Hamdulla, Wujiahemaiti Simayi, Mayire Ibrayim, Dilmurat Tursun
Published: 1 January 2014
by 10.1007
Communications in Computer and Information Science pp 474-480; https://doi.org/10.1007/978-3-662-45643-9_50

The publisher has not yet granted permission to display this abstract.
Ahmed. T. Sahlol, Ching Y. Suen, Mohammed R. Elbasyoni, Abdelhay A. Sallam
Published: 1 January 2014
by 10.1007
Lecture Notes in Computer Science pp 264-276; https://doi.org/10.1007/978-3-319-11656-3_24

The publisher has not yet granted permission to display this abstract.
Mustafa Ali Abuzaraida, Akram M. Zeki
2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M) pp 1-7; https://doi.org/10.1109/ict4m.2013.6518884

Abstract:
Online recognition of Arabic handwritten text has been an ongoing research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. Most of the online text recognition systems consist of three main phases which are preprocessing, feature extraction, and recognition phase. This paper compares between different techniques that have been used to extract the features of Arabic handwriting scripts in online recognition systems. Those techniques attempt to extract the feature vector of Arabic handwritten words, characters, numbers or strokes. This vector then will be fed into the recognition engine to recognize the pattern using the feature vector. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.
Hui Zhao, Wenping Ge
Abstract:
Online handwriting recognition is a very important part in the field of pattern recognition. Up to now there are few reports about the online Uyghur word handwriting recognition. This paper proposes an effective method based on HMM to recognize handwritten Uyghur word. This method introduces a feature extraction approach and presents a language model, resulting in a higher accuracy.
Mongi Hammadi, Hala Bezine, Sourour Njah,
International Conference on Education and e-Learning Innovations pp 1-6; https://doi.org/10.1109/iceeli.2012.6360661

Abstract:
In today many students produce a wrong and illegible handwriting. The traditional approach for handwriting teaching needs a long hour of handwriting practice, and teacher needs a lot of time to check the handwriting errors. Unfortunately, this is not feasible in many cases. In this paper, we introduce an automated educational tool for Arabic Handwriting detection errors, such as the stroke production errors, stroke sequence errors, stroke relationship errors and stroke interline errors, to help students to generate clear handwriting. Firstly, we used an attributed relational graph to locate the handwriting errors. Secondly, an immediate feedback is provided to the students to correct them.
International Journal on Document Analysis and Recognition (IJDAR), Volume 16, pp 209-226; https://doi.org/10.1007/s10032-012-0186-8

The publisher has not yet granted permission to display this abstract.
Sherif Abdel Azeem, Hany Ahmed
2011 10th International Conference on Machine Learning and Applications and Workshops, Volume 1, pp 204-207; https://doi.org/10.1109/icmla.2011.120

Abstract:
The aim of this work is to fill a void in the literature of Arabic handwriting recognition by studying the performance of different feature extraction methods on online segmented Arabic characters. The contribution of this paper is to introduce a large database of segmented online handwritten Arabic characters and report the performance of various feature extraction techniques on the segmented characters to serve as a benchmark for any future work on the problem of online Arabic characters recognition.
Twana N. Abdullah, , Mohammad Faidzul Nasrudin
2011 International Conference on Pattern Analysis and Intelligence Robotics, Volume 1, pp 83-86; https://doi.org/10.1109/icpair.2011.5976916

Abstract:
Moment Invariant (MI) has been frequently used as feature for shape recognition. These features are invariant to several deformations such as rotation, scaling and translation. However it is sensitive to distortions that primarily affect the `centre of gravity' of the image. Images of an Arabic Word might have different centroid due to the fact that it might be written using different Handwriting styles. In this paper we examine the effect of replacing the image centroid with the center of image as the reference point in Moment Invariant (MI). The new descriptors set was tested to recognize Arabic Words based on IFN/ENIT Database that consisting of 26459 words written by 411 different writers. The Back Propagation Neural Network was used as the classifier. Experiment results had shown that by using the new descriptors the average recognition accuracy has increased by 18.38%.
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