Abstract
Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost.
Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature selection technique for handwritten digit recognition.
Methods: A hybrid feature set of statistical and geometrical features is developed in order to get the effective feature set consist of local and global characteristics of sample digits. The method utilizes a genetic algorithm based feature selection for selecting best distinguishable features and k-nearest neighbour for evaluating the fitness of features of handwritten digit dataset.
Results: The experiments are carried out on standard The Chars74K handwritten digit dataset and reported a 66% reduction in the original feature set without sacrificing the recognition accuracy.
Conclusion: The experiment results show the effectiveness of the proposed approach.
Keywords: Feature reduction, neural networks, feature selection, genetic algorithm, digit recognition, recognition problem.
Graphical Abstract
[1]
H. Liu, and L. Yu, "Toward integrating feature selection algorithms for classification and clustering", IEEE Trans. Knowl. Data Eng., vol. 17, pp. 491-502, 2005. [http://dx.doi.org/10.1109/TKDE.2005.66].
[2]
S. Goswami, A.K. Das, A. Chakrabarti, and B. Chakraborty, "A feature cluster taxonomy based feature selection technique", Expert Syst. Appl., vol. 79, pp. 76-89, 2017. [http://dx.doi.org/10.1016/j.eswa.2017.01.044].
[3]
L. Wang, Y. Wang, and Q. Chang, "Feature selection methods for big data bioinformatics: A survey from the search perspective", Methods, vol. 111, pp. 21-31, 2016. [http://dx.doi.org/10.1016/j.ymeth.2016.08.014]. [PMID: 27592382].
[4]
H. Liu, and Z. Zhao, "Manipulating data and dimension reduction methods: Feature selection", In: Encyclopedia of Complexity and Systems Science., Springer, 2009, pp. 5348-5359. [http://dx.doi.org/10.1007/978-0-387-30440-3_317]
[5]
H. Liu, H. Motoda, R. Setiono, and Z. Zhao, "Feature selection: An ever evolving frontier in data mining", In: Feature Selection Data Mining JMLR Proceedings, vol. 10. pp. 4-13. 2010
[6]
M. Dash, and H. Liu, "Feature selection for classification", Intell. Data Anal., vol. 1, no. 4, pp. 131-156, 1997. [http://dx.doi.org/10.3233/IDA-1997-1302].
[7]
L.X. Zhang, J.X. Wang, Y.N. Zhao, and Z.H. Yang, "A novel hybrid feature selection algorithm: using Relief estimation for GA-Wrapper search", In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), Xian, China 2010, pp. 380-384. [http://dx.doi.org/10.1109/ICMLC.2003.1264506]
[8]
J. Shen, J. Xia, X. Zhang, and W. Jia, "Sliding block-based hybrid feature subset selection in network traffic", IEEE Access, vol. 5, pp. 18179-18186, 2017. [http://dx.doi.org/10.1109/ACCESS.2017.2750489].
[9]
Q. Song, J. Ni, and G. Wang, "A fast clustering-based feature subset selection algorithm for high-dimensional data", IEEE Trans. Knowl. Data Eng., vol. 25, no. 1, pp. 1-14, 2013. [http://dx.doi.org/10.1109/TKDE.2011.181].
[10]
M.Y. Chen, A. Kundu, and J. Zhou, "Off-line handwritten word recognition using a hidden Markov model type stochastic network", IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 5, pp. 481-496, 1994. [http://dx.doi.org/10.1109/34.291449].
[11]
J.Y. Liang, F. Wang, C.Y. Dang, and Y.H. Qian, "A group incremental approach to feature selection applying rough set technique", IEEE Trans. Knowl. Data Eng., vol. 26, no. 2, pp. 294-308, 2014. [http://dx.doi.org/10.1109/TKDE.2012.146].
[12]
L. Vermeulen-Jourdan, C. Dhaenens, and E.G. Talbi, Clustering Nominal and Numerical Data: A New Dis-tance Concept for a Hybrid Genetic Algorithm. Evolutionary Computation in Combinatorial Optimization. EvoCOP., Springer: Berlin, Heidelberg, 2004. [http://dx.doi.org/10.1007/978-3-540-24652-7_22]
[13]
D. Oreski, S. Oreski, and B. Klicek, "Effects of dataset characteristics on the performance of feature selection techniques", Appl. Soft Comput., vol. 52, pp. 109-119, 2017. [http://dx.doi.org/10.1016/j.asoc.2016.12.023].
[14]
M.Y. Kiang, "A comparative assessment of classification methods", Decis. Support Syst., vol. 35, pp. 441-454, 2003. [http://dx.doi.org/10.1016/S0167-9236(02)00110-0].
[15]
D.M.J. Tax, and R.P.W. Duin, "Characterizing one-class datasets", In: Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, 2005, pp. 21-26.
[16]
V. Bolon-Canedo, N. Sanchez-Marono, and A. Alonso-Betanzos, "Recent advances and emerging challenges of feature selection in the context of big data", Knowl. Base. Syst., vol. 86, pp. 33-45, 2015. [http://dx.doi.org/10.1016/j.knosys.2015.05.014].
[17]
Y. Liu, G. Wang, H. Chen, and H. Dong, "An improved particle swarm optimization for feature selection", J. Bionics Eng., vol. 8, no. 2, pp. 191-200, 2011. [http://dx.doi.org/10.1016/S1672-6529(11)60020-6].
[18]
S. Zilberstein, "Using anytime algorithms in intelligent systems", AI Mag., vol. 17, no. 3, pp. 73-83, 1996.
[19]
M. Alshraideh, B. Mahafzah, H. Salman, and I. Salah, "Using genetic algorithm as test data generator for stored PL/SQL program units", J. Software Eng. Appl., vol. 6, no. 2, pp. 65-73, 2013. [http://dx.doi.org/10.4236/jsea.2013.62011].
[20]
M. Alshraideh, E. Jawabreh, B. Mahafzah, and H. Harahsheh, "Applying genetic algorithms to test JUH DBs exceptions", Intl. J. Adv. Comput. Sci. Appl., vol. 4, pp. 8-20, 2013. [http://dx.doi.org/10.14569/IJACSA.2013.040702].
[21]
M. Alshraideh, B. Mahafzah, and S.A. Sharaeh, "A multiple-population genetic algorithm for branch coverage test data generation", Softw. Qual. J., vol. 19, no. 3, pp. 489-513, 2011. [http://dx.doi.org/10.1007/s11219-010-9117-4].
[22]
I. Guyon, and A. Elisseeff, "An introduction to variable and feature selection", J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
[24]
A. Choudhary, R. Rishi, and S. Ahlawat, "Off-line handwritten character recognition using features extracted from binarization technique", AASRI Procedia., vol. 4, pp. 306-312, 2013. [http://dx.doi.org/10.1016/j.aasri.2013.10.045].
[25]
B. Xue, M. Zhang, W.N. Browne, and X. Yao, "A survey on evolutionary computation approaches to feature selection", IEEE Trans. Evol. Comput., vol. 20, no. 4, pp. 606-626, 2016. [http://dx.doi.org/10.1109/TEVC.2015.2504420].
[26]
R. Kohavi, and G.H. John, "Wrappers for feature subset selection", Artif. Intell., vol. 97, no. 1-2, pp. 273-324, 1997. [http://dx.doi.org/10.1016/S0004-3702(97)00043-X].
[27]
K. Kira, and L.A. Rendell, "The feature selection problem: traditional methods and a new algorithm.In:", Proceedings. AAAI 92. 1992, pp. 129-134
[28]
C.K. Lee, and G.G. Lee, "Information gain and divergence-based feature selection for machine learning-based text categorization", Inf. Process. Manage., vol. 42, pp. 155-165, 2006. [http://dx.doi.org/10.1016/j.ipm.2004.08.006].
[29]
K.M. Kim, J.J. Park, Y.G. Song, I.C. Kim, and C.Y. Suen, Recognition of handwritten numerals using a combined classifier with hybrid features.SSPR&SPR 2004”, LNCS., Springer: Berlin Vol. 3138, pp. 992-1000. 2004 [https://doi.org/10.1007/978-3-540-27868-9_109]
[30]
J. Sadria, C.Y. Suena, and T.D. Buib, "A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings", Pattern Recognit., vol. 40, no. 3, pp. 898-919, 2007. [http://dx.doi.org/10.1016/j.patcog.2006.08.002].
[31]
D. Koller, and M. Sahami, "Toward optimal feature selection", In: 13th International Conference on Machine Learning (ML), San Francisco, CA, USA 1996, pp. 284-292.
[32]
P. Langley, and S. Sage, "Induction of selective bayesian classifiers", In: 10th Conference on Uncertainty in Artificial gence , San Francisco, CA, USA 1994, pp. 399-406.
[34]
P. Singh, A. Verma, and N. Chaudhari, "Feature selection based classifier combination approach for handwritten Devanagari numeral recognition", Sadhana Indian Academy of Sciences, vol. 40 2015, pp. 1701-1714, .
[35]
F. Wang, and J. Liang, "An efficient feature selection algorithm for hybrid data", Neurocomputing, vol. 193, pp. 33-41, 2016. [http://dx.doi.org/10.1016/j.neucom.2016.01.056].
[36]
M.L. Raymer, W.F. Punch, E.D. Goodman, L.A. Kuhn, and A.K. Jain, "Dimensionality reduction using genetic algorithms", IEEE Trans. Evol. Comput., vol. 4, no. 2, pp. 164-171, 2000. [http://dx.doi.org/10.1109/4235.850656].
[37]
H. Lu, J. Chen, K. Yan, Q. Jin, Y. Xue, and Z. Gao, "A hybrid feature selection algorithm for gene expression data classification", Neurocomputing, vol. 256, pp. 56-62, 2017. [http://dx.doi.org/10.1016/j.neucom.2016.07.080].
[38]
D. Kleftogiannis, K. Theofilatos, S. Likothanassis, and S. Mavroudi, "YamiPred: A novel evolutionary method for predicting pre-mirnas and selecting relevant features", IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. 12, no. 5, pp. 1183-1192, 2015. [http://dx.doi.org/10.1109/TCBB.2014.2388227]. [PMID: 26451829].
[39]
M.K. Masood, C. Jiang, and Y.C. Soh, "A novel feature selection framework with Hybrid Feature-Scaled Extreme Learning Machine (HFS-ELM) for indoor occupancy estimation", Energy Build., vol. 158, pp. 1139-1151, 2018. [http://dx.doi.org/10.1016/j.enbuild.2017.08.087].
[40]
J.Y. Liang, F. Wang, C.Y. Dang, and Y.H. Qian, "An efficient rough feature selection algorithm with a multi-granulation view", Int. J. Approx. Reason., vol. 53, pp. 912-926, 2012. [http://dx.doi.org/10.1016/j.ijar.2012.02.004].
[41]
A. Choudhary, R. Rishi, and S. Ahlawat, Unconstrained Handwritten Digit OCR Using Projection Profile and Neural Network Approach S. Satapathy, P. S. Avadhani and A. Abraham, Eds. In: Proceedings of the International Conference on Information Systems Design and Intelligent Applications (INDIA 2012), 2012, pp. 119-126.
[42]
F. Ghareh Mohammadi, and M. Saniee Abadeh, "Image steganalysis using a bee colony based feature selection algorithm", Eng. Appl. Artif. Intell., vol. 31, pp. 35-43, 2014. [http://dx.doi.org/10.1016/j.engappai.2013.09.016].
[43]
A. Roy, N. Das, R. Sarkar, S. Basu, M. Kundu, and M. Nasipuri, "An axiomatic fuzzy set theory based feature selection methodology for handwritten numeral recognition", Proceedings of the 48th Annual Convention of Computer Society of India, vol. 248, 2014pp. 133-140
[44]
M. Arif Mohamad, H. Hassan, D. Nasien, and H. Haron, "A review on feature extraction and feature selection for hand written character recognition", Intl. J. Adv. Comput. Sci. Appl., vol. 6, pp. 204-212, 2015. [http://dx.doi.org/10.14569/IJACSA.2015.060230].
[45]
L.S. Oliveira, N. Benahmed, R. Sabourin, F. Bortolozzi, and C.Y. Suen, "Feature subset selection using genetic algorithms for handwritten digit recognition", In: Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing, Florianopolis, Brazil 2001, pp. 362-369. [http://dx.doi.org/10.13140/2.1.1867.2328]
[46]
L.S. Oliveira, M. Morita, and R. Sabourin, "Feature selection for ensembles applied to handwriting recognition", Intl. J. Document Analysis, vol. 8, no. 4, pp. 262-279, 2006. [http://dx.doi.org/10.1007/s10032-005-0013-6].
[47]
D. Zongker, and A. Jain, Algorithms for feature selection: An evaluationProceedings of 13th International Conference on Pattern Recognition, Vienna, Austria 1996, pp. 18-22. [http://dx.doi.org/10.1109/ICPR.1996.546716]
[48]
N. Das, J.M. Reddy, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri, and D.K. Basu, "A statistical-topological feature combination for recognition of handwritten numerals", Appl. Soft Comput., vol. 12, no. 8, pp. 2486-2495, 2012. [http://dx.doi.org/10.1016/j.asoc.2012.03.039].
[49]
C. De-Stefano, F. Fontanella, C. Marrocco, and A. Scotto di Freca, "A GA-based feature selection approach with an application to handwritten character recognition", Pattern Recognit. Lett., vol. 35, pp. 130-141, 2014. [http://dx.doi.org/10.1016/j.patrec.2013.01.026].
[50]
G. Katiyar, and S. Mehfuz, "A hybrid recognition system for off-line handwritten characters", Springerplus, vol. 5, pp. 357-374, 2016. [http://dx.doi.org/10.1186/s40064-016-1775-7]. [PMID: 27066370].
[51]
Y. Chherawala, P.P. Roy, and M. Cheriet, "Feature set evaluation for offline handwriting recognition systems: Application to the recurrent neural network model", IEEE Trans. Cybern., vol. 46, no. 12, pp. 2825-2836, 2016. [http://dx.doi.org/10.1109/TCYB.2015.2490165]. [PMID: 26561491].
[52]
A. Jalilvand, and N. Salim, "Feature unionization: A novel approach for dimension reduction", Appl. Soft Comput., vol. 52, pp. 1253-1261, 2017. [http://dx.doi.org/10.1016/j.asoc.2016.08.031].
[53]
J. Apolloni, G. Leguizamón, and E. Alba, "Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments", Appl. Soft Comput., vol. 38, pp. 922-932, 2016. [http://dx.doi.org/10.1016/j.asoc.2015.10.037].
[54]
F. Min, Q. Hu, and W. Zhu, "Feature selection with test cost constraint", Intl. J. Approximate Reasoning., vol. 55, no. 1, pp. 167-179, 2014. [http://dx.doi.org/10.1016/j.ijar.2013.04.003].
[55]
Y. Liu, F. Tang, and Z. Zeng, "Feature selection based on dependency margin", IEEE Trans. Cybern., vol. 45, no. 6, pp. 1209-1221, 2015. [http://dx.doi.org/10.1109/TCYB.2014.2347372]. [PMID: 25265639].
[56]
N.D. Cilia, C. De Stefano, F. Fontanella, and A.S. di Freca, "A ranking-based feature selection approach for handwritten character recognition", Pattern Recognit. Lett., vol. 121, pp. 77-86, 2018. [https://doi.org/10.1016/j.patrec.2018.04.007].
[57]
Y. Yang, and J.O. Pedersen, "A comparative study on feature selection in text categorization", In: Proceedings of the 14th International Conference on Machine Learning, San Francisco, CA, USA 1997, pp. 412-420.
[58]
G. Forman, "An extensive empirical study of feature selection metrics for text classification", J. Mach. Learn. Res., vol. 3, pp. 1289-1305, 2003.
[59]
H. Liu, J. Sun, L. Liu, and H. Zhang, "Feature selection with dynamic mutual information", Pattern Recognit., vol. 42, no. 7, pp. 1330-1339, 2009. [http://dx.doi.org/10.1016/j.patcog.2008.10.028].
[60]
H. Liu, L. Liu, and H. Zhang, "Boosting feature selection using information metric for classification", Neurocomputing, vol. 73, no. 1-3, pp. 295-303, 2009. [http://dx.doi.org/10.1016/j.neucom.2009.08.012].
[61]
W. Homenda, and A. Jastrzebska, "A practical study on feature selection methods in pattern recognition: Examples of handwritten digits and printed musical notation", In: 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Hamamatsu, Japan 2017, pp. 1035-1038. [http://dx.doi.org/10.1109/IIAI-AAI.2017.186]
[62]
T.E. de Campos, B.R. Babu, and M. Varma, "Character recognition in natural images", In: Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP)Lisbon, Portugal 2009, .
[63]
D. Khanduja, N. Nain, and S. Panwar, "“A hybrid feature extraction algorithm for devanagari script”, ACM Trans. Asian Low-Resour", Lang. Inf. Process, vol. 15, no. 1, pp. 1-10, 2015. [http://dx.doi.org/10.1145/2710018].
[64]
S. Ahlawat, and R. Rishi, "Off-line handwritten numeral recognition using hybrid feature Set-A comparative analysis", Procedia Comput. Sci., vol. 122, pp. 1092-1099, 2017. [http://dx.doi.org/10.1016/j.procs.2017.11.478].
[65]
G. Singh, A. Pokhriyal, and S. Lehri, "Neuro-fuzzy model based classification of handwritten hindi modifiers", Intl. J. Appl. Innovation Eng. Manage., vol. 3, no. 6, pp. 311-325, 2014.
[66]
S. Mori, C.Y. Suen, and K. Yamamoto, "Historical review of OCR research and development", Proc. IEEE, vol. 80, pp. 1029-1057, 1992. [http://dx.doi.org/10.1109/5.156468].
[67]
M.H. Glauberman, "Character recognition for business machines", Electronics (Basel), pp. 132-136, 1956.
[68]
R. Sheikhpour, M.A. Sarram, and R. Sheikhpour, "Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer", Appl. Soft Comput., vol. 40, pp. 113-131, 2016. [http://dx.doi.org/10.1016/j.asoc.2015.10.005].
[69]
A.L. Blum, and P. Langley, "Selection of relevant features and examples in machine learning", Artif. Intell., vol. 97, pp. 245-271, 1997. [http://dx.doi.org/10.1016/S0004-3702(97)00063-5].
[70]
H. Liu, and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining., Springer Science & Business Media Vol. 454, 1998. [http://dx.doi.org/10.1007/978-1-4615-5689-3]
[71]
A.A. Altun, and N. Allahverdi, Neural Network Based Recognition by Using Genetic Algorithm for Feature Selection of Enhanced Fingerprints. Adaptive and Natural Computing Algorithms. ICANNGA., Springer: Berlin, Heidelberg Vol. 4432, 2007. [http://dx.doi.org/10.1007/978-3-540-71629-7_53]
[72]
M. Liu, and D. Zhang, "Feature selection with effective distance", Neurocomputing, vol. 2015, pp. 100-109, 2016. [http://dx.doi.org/10.1016/j.neucom.2015.07.155].
[73]
Y. Yi, W. Zhou, Q. Liu, G. Luo, J. Wang, Y. Fang, and C. Zheng, "Ordinal preserving matrix factorization for unsupervised feature selection", Signal Process. Image Commun., vol. 67, pp. 118-131, 2018. [http://dx.doi.org/10.1016/j.image.2018.06.005].
[74]
O. Boyabatli, and I. Sabuncuoglu, "Parameter Selection in Genetic Algorithms", J. Syst. Cybern. Inf., vol. 4, no. 2, pp. 78-83, 2004.
[75]
A. Rexhepi, A. Maxhuni, and A. Dika, "Analysis of the impact of parameters values on the Genetic Algorithm for TSP", IJCSI Int. J. Computer Sci.Issues., vol. 10, no. 2013, pp. 158-164. 2014
[76]
K. Sastry, D.E. Goldberg, and G. Kendall, "Genetic algorithms: A tutorial", In: Introductory Tutorials in Optimization, Search and Decision Support Methodologies., Springer, 2005, pp. 97-125.
[77]
A. Alajmi, and J. Wright, "Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem", Intl. J. Sustainable Built Environ., vol. 3, no. 1, pp. 18-26, 2014. [http://dx.doi.org/10.1016/j.ijsbe.2014.07.003].
[78]
L. Haupt, “Optimum population size and mutation rate for a simple real genetic algorithm that optimizes array factors”, In: IEEE Antennas and Propagation Society International SymposiumTransmitting Waves Progress Next Millennium, vol. 2. pp. 1034-1037. 2000 [http://dx.doi.org/10.1109/APS.2000.875398]
[79]
C. Freeman, D. Kulic, and O. Basir, "An evaluation of classifier-specific filter measure performance for feature selection", Pattern Recognit., vol. 48, pp. 1812-1826, 2015. [http://dx.doi.org/10.1016/j.patcog.2014.11.010].
[80]
S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri, and D.K. Basu, "A novel framework for automatic sorting of postal documents with multi-script address blocks", Pattern Recognit., vol. 43, pp. 3507-3521, 2010. [http://dx.doi.org/10.1016/j.patcog.2010.05.018].
[81]
S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri, and D. Basu, Recognition of numeric postal codes from multi-script postal address blocks. Pattern Recognition and Machine Intelligence., Springer: Berlin, Heidelberg, 2009, pp. 381-386. [http://dx.doi.org/10.1007/978-3-642-11164-8_62]
[82]
H. Wei, K. Chen, R. Ingold, and M. Liwicki, "Hybrid Feature Selection for Historical Document Layout Analysis", In: 14th International Conference on Frontiers in Handwriting Recognition, Heraklion, Greece 2014, pp. 87-92. [http://dx.doi.org/10.1109/ICFHR.2014.22]
[83]
L.R. Veloso, and J.M. de Carvalho, "Neural versus syntactic recognition of handwritten numerals", In: Proceedings of the Fifth International Conference on Document Analysis and Recognition, ICDAR’99, Bangalore, India 1999, pp. 233-236. [http://dx.doi.org/10.1109/ICDAR.1999.791767]
[84]
G. Abandah, and N. Anssari, "Novel Moment Features Extraction for Recognizing Handwritten Arabic Letters", J. Comput. Sci., vol. 5, no. 3, pp. 226-232, 2009. [http://dx.doi.org/10.3844/jcssp.2009.226.232].
[85]
N. Das, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri, and D.K. Basu, "A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application", Appl. Soft Comput., vol. 12, no. 5, pp. 1592-1606, 2012. [http://dx.doi.org/10.1016/j.asoc.2011.11.030].
[86]
A.J. Newell, and L.D. Griffin, "Multiscale histogram of oriented gradient descriptors for robust character recognition", In: International Conference on Document Analysis and Recognition (ICDAR), Beijing, China 2011, pp. 1085-1089. [http://dx.doi.org/10.1109/ICDAR.2011.219]
[87]
T. Kailath, The divergence and Bhattacharyya distance measures in signal selection IEEE Trans. Commun. Technol., COM- 15., vol. 1. 1967. [http://dx.doi.org/10.1109/TCOM.1967.1089532], pp. 52-60.
[88]
Y. Kimura, A. Suzuki, and K. Odaka, "Feature selection for character recognition using genetic algorithm", In: Fourth International Conference on Innovative Computing, Information and Control (ICICIC), Kaohsiung, Taiwan 2009, pp. 401-404. [http://dx.doi.org/10.1109/ICICIC.2009.210]
[89]
M. Soomro, M.A. Farooq, and R.H. Raza, "Performance evaluation of advanced deep learning architectures for offline handwritten character recognition", In: International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan 2017, pp. 362-367. [http://dx.doi.org/10.1109/FIT.2017.00071]
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