Improved HMM for Cursive Arabic Handwriing Recognition System using MLP Classifier
Keywords:Arabic Handwriting Recognition, Context, Embedded training, HMMs, Multilayer Perceptron (MLP).
Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems.The proposed work reports an effective method on improvement our previous work that takes into consideration the context of character by applying an embedded training based HMMs this HMM is enhanced by an Artificial neural network that are incorporated into the process of classification to estimate the emission probabilities. The experiments are done on the same benchmark IFN/ENIT database of our previous work to compare the results and show the effectiveness of hybrid classifier for enhancing the recognition rate the results are promising and encouraging.
(1) J. H. Alkhateeb O. Pauplin J. Ren J. Jiang Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition In Knowledge-Based Systems 24 (2011) pp.680-688.
(2) Salvador Espan˜a-Boquera Maria Jose Castro-Bleda Jorge Gorbe-Moya and Francisco Zamora-Martinez “Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN models” 2011
(3) T.Plots and G.A.Fink; “ Markov models for offline handwriting recognition: a survey” IntJ.Anal.Recongitvol 12 no 4 pp 269-298 2009.
(4) Irfan Ahmad GernotA.Fink and SabriA.Mahmoud “Improvement in Sub-character HMM Model Based Arabic Text Recognition” 2014 14th International Conference on frontiers in Handwrting Recognition.
(5) S. Azeem and H. Ahmed “Effective technique for the recognition of offline Arabic handwritten words using hidden Markov models” Int. J. Doc. Anal.Recognit.vol. 16 no. 4 pp.399–412 2013.
(6) J. AlKhateeb H. RenJinchang Jiang Jianmin Al-MuhtasebHusni Offline handwritten arabic cursive text recognition using hidden markov models and re-ranking Pattern Recognition Lett. 32 (8) 1081–1088 2011.
(7) Y. Kessentini T. Paquet A. Ben Hamadou Off-line handwritten word recognition using multistream hidden Markov models in: Pattern Recognition Letters 31 (2010) pp. 60-70
(8) A. Maqqor A. Halli K. Satori and H. Tairi Off-line recognition Handwriting combination of mutiple classifiers In 3rd International IEEE Colloquium on Information Science and Technology IEEE CIST’14 October 2014.
(9) El MoubtahijHichamHalliAkram Khalid Satori “Using features of local densities statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition” 2016
(10) KhaoulaJayech Mohamed Ali Mahjoub and Najoua Ben Amara “Arabic Handwritten Word Recognition based on Dynamic Bayesian Network” 2016.
(11) Berend-Jan van der Zwaag “Handwritten Digit Recognition: A Neural Network Demo” 2016
(12) Xu Chen Convolution Neural Networks for Chinese Handwriting Recognition 2016.
(13) Charlie Tsai “Recognizing Handwritten Japanese Characters Using Deep Convolutional Neural Networks” 2016
(14) [ThéodoreBluche “Deep Neural Networks for Large Vocabulary Handwritten Text Recognition” 2015
(15) Ahmed Mahdi ObaidHazem M. El Bakry M.A. Eldosuky IVA.I.Shehab “Handwritten Text Recognition System Based on Neural Network” 2016
(16) AL-Shatnawi M. Atallah AL-SalaimehSafwan AL-Zawaideh Farah Hanna Omar Khairuddin “Offline arabic text recognition an overview” World Comput. Sci. Inform. Technol. J. 1 (5) 184–192 2011.
(17) M.T Parvez and S.A Mahmoud “ Offline Arabic handwritten text recognition : A survey “ ACM ComputSurvvol 45 no 2 pp 23-35 May 2013.
(18) A.Lawgali : “A Survey on Arabic Character Recognition” International Journal of Signal Processing Image Processing and Pattern Recognition. Vol. 8 No. 2 (2015) pp. 401-426.
(19) M.RabiM.AmrouchZ.MahaniD.Mammass “Recognition of cursive Arabic handwritten text using embedded training based on HMMs” Published in: Engineering & MIS (ICEMIS) International Conference on Sept. 2016 INSPEC Accession Number: 16467172 DOI: 10.1109/ICEMIS.2016.7745330 Publisher: IEEE
(20) Mohamed ETT AOUIL Mohamed LAZAAR Zakariae EN-NAIMANI “A hybrid ANN/HMM models for arabic speech recognition using optimal codebook” 8th International Conference on Intelligent Systems: Theories and Applications (SITA), IEEE 2013.
(21) Supriya S. Surwade “Speech Recognition Using HMM/ANN Hybrid Model” International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 3 Issue: 6 4154 – 4157.2015
(22) A. I. G-Moral, U. S-Urena, C. P-Moreno and F. D-Maria, 2011, “Data balancing for efficient training of hybrid ANN/HMM automatic speech recognition, ” IEEE trans. on audio, speech and lang. proc., Vol 19, No. 3, 468-481.
(23) Anuj Mohamed K.N. Ramachandran Nair “HMM/ANN hybrid model for continuous Malayalam speech recognition” Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2012 (International Conference on Communication Technology and System Design).
(24) E. Trentin, M. Gori, 2003, “Robust combination of neural networks and Hidden Markov Models for speech recognition,” IEEE trans. on Neural net., Vol 14 , No. 6, 1519-1531.
(25) NajibaTagouguiHoucineBoubakerMonjiKherallah Adel M. ALIMI “A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition” International Journal of Computational Linguistics Research Volume 4 Nimber 3 Septembre 2013 pp 107-118.
(26) M.RabiM.AmrouchZ.MahaniD.Mammass” Evaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition ” Global Journal of Computer Science and Technology (USA),: C Software & Data Engineering, Volume 16 Issue 5 Version 1.0 Year 2016.
(27) Dreuw P. Doetsch P. Plahl C. Ney H. (2011). Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition Image Processing (ICIP) 18th IEEE International Conference on p. 3541-3544 Sept.
(28) S Espana-Boquera MJ Castro-Bleda J Gorbe-Moya F Zamora-Martinez “Improving offline handwritten text recognition with hybrid HMM/ANN models” IEEE transactions on pattern analysis and machine intelligence 33 (4) 767-779
(29) Q.GuoF.WangJ.LeiD.TuG.Li “Convolutional feature learning and Hybrid CNN-HMM for scene number recognition” Published in journal Neucomputing archive Volume 184 Issue C April 2016 Pages 78-90 Elsevier Science Publishers B.V Amsterdam The Netherlands.
(30) Bluche T. Ney H. &Kermorvant C. (2013b).Tandem HMM with convolutional neural network for handwritten word recognition.In 17th International Conference on Acoustics Speech and Signal Processing (ICASSP) (pp. 2390--2394).IEEE.
(31) M. Pechwitz, S.S. Maddouri, V. Maergner, N. Ellouze, H. Amiri IFN/ENIT – Database of Handwritten Arabic WordsCIFED 2002, Hammamet, Tunisia (2002), pp. 129–136
(32) Märgner V. El Abed H.: ICDAR 2011 - Arabic handwriting recognition competition. In: Int’l Conf. Document Analysis and
Recognition pp. 1444–1448 (2011)
(33) S. Young al. the HTK Book V3.4 Cambridge University Press Cambridge UK 2006