Recognition of handwritten digits by image processing methods and classification models
OCR (Optical Character Recognition) is a line of research within image processing for which many techniques and methodologies have been developed. Set of pixels recognized based on the digitalized image and this study presents an iterative process that consists of five phases of the OCR. For this pu...
- Autores:
-
amelec, viloria
Rico, Reinaldo
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7271
- Acceso en línea:
- https://hdl.handle.net/11323/7271
https://repositorio.cuc.edu.co/
- Palabra clave:
- Classification models
Genetic algorithm
Image processing
Methods
Recognition of handwritten digits
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Recognition of handwritten digits by image processing methods and classification models |
| title |
Recognition of handwritten digits by image processing methods and classification models |
| spellingShingle |
Recognition of handwritten digits by image processing methods and classification models Classification models Genetic algorithm Image processing Methods Recognition of handwritten digits |
| title_short |
Recognition of handwritten digits by image processing methods and classification models |
| title_full |
Recognition of handwritten digits by image processing methods and classification models |
| title_fullStr |
Recognition of handwritten digits by image processing methods and classification models |
| title_full_unstemmed |
Recognition of handwritten digits by image processing methods and classification models |
| title_sort |
Recognition of handwritten digits by image processing methods and classification models |
| dc.creator.fl_str_mv |
amelec, viloria Rico, Reinaldo Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Rico, Reinaldo Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Classification models Genetic algorithm Image processing Methods Recognition of handwritten digits |
| topic |
Classification models Genetic algorithm Image processing Methods Recognition of handwritten digits |
| description |
OCR (Optical Character Recognition) is a line of research within image processing for which many techniques and methodologies have been developed. Set of pixels recognized based on the digitalized image and this study presents an iterative process that consists of five phases of the OCR. For this purpose, several image processing methods are applied, as well as two variable selection methods, and several supervised automated learning methods are explored. Among the classification models, those of deep learning stand out for their novelty and enormous potential. |
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2020 |
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2020-11-11T22:46:13Z |
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2020-11-11T22:46:13Z |
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2020 |
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2021-05-07 |
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Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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2194-5357 |
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https://hdl.handle.net/11323/7271 |
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Corporación Universidad de la Costa |
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eng |
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| dc.relation.references.spa.fl_str_mv |
Boufenar, C., Kerboua, A., Batouche, M.: Investigation on deep learning for off-line handwritten Arabic character recognition. Cogn. Syst. Res. 50, 180–195 (2018) Dasgupta, J., Bhattacharya, K., Chanda, B.: A holistic approach for Off-line handwritten cursive word recognition using directional feature based on Arnold transform. Pattern Recogn. Lett. 79, 73–79 (2016) Jangid, M., Srivastava, S.: Handwritten devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. J. Imaging 4(2), 41 (2018) Tarawneh, A.S., Hassanat, A.B., Chetverikov, D., Lendak, I., Verma, C.: Invoice classification using deep features and machine learning techniques. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 855–859. IEEE, April 2019 Niu, X.X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012) Wang, Z., Wang, R., Gao, J., Gao, Z., Liang, Y.: Fault recognition using an ensemble classifier based on Dempster-Shafer Theory. Pattern Recogn. 99, 107079 (2020) Zhou, B., Ghose, T., Lukowicz, P.: Expressure: detect expressions related to emotional and cognitive activities using forehead textile pressure mechanomyography. Sensors 20(3), 730 (2020) Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015) Mohiuddin, K., Mao, J.: A comparative study of different classifiers for handprinted character recognition. Pattern Recogn. Practice IV, 437–448 (2014) Le Cun, Y., Cortes, C.: MNIST handwritten digit database. AT&T Labs. http://yann.lecun.com/exdb/mnist. Accessed: 20 Dec 2019 Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019) Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019) Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020) Koresh, M.H.J.D., Deva, J.: Computer vision based traffic sign sensing for smart transport. J. Innov. Image Process. (JIIP) 1(01), 11–19 (2019) Zhang, B., Fu, M., Yan, H.: A nonlinear neural network model of mixture of local principal component analysis: application to handwritten digits recognition. Pattern Recogn. 34(2), 203–214 (2001) Ghosh, A., Pavate, A., Gholam, V., Shenoy, G., Mahadik, S.: Steady model for classification of handwritten digit recognition. In: Sharma, R., Mishra, M., Nayak, J., Naik, B., Pelusi, D. (eds.) Innovation in Electrical Power Engineering, Communication, and Computing Technology. LNEE, vol. 630, pp. 401–412. Springer, Singapore (2020). https://ezproxy.cuc.edu.co:2067/10.1007/978-981-15-2305-2_32 Garg, A., Gupta, D., Saxena, S., Sahadev, P.P.: Validation of random dataset using an efficient CNN model trained on MNIST handwritten dataset. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 602–606. IEEE, March 2019 El-Sawy, A., Hazem, E.B., Loey, M.: CNN for handwritten arabic digits recognition based on LeNet-5. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 566–575. Springer, Cham, October 2016 Paul, O.: Image pre-processing on NumtaDB for Bengali handwritten digit recognition. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–6. IEEE, September 2018 Shamsuddin, M.R., Abdul-Rahman, S., Mohamed, A.: Exploratory analysis of MNIST handwritten digit for machine learning modelling. In: International Conference on Soft Computing in Data Science, pp. 134–145. Springer, Singapore, August 2018 Pujari, P., Majhi, B.: Recognition of Odia handwritten digits using gradient based feature extraction method and clonal selection algorithm. Int. J. Rough Sets Data Anal. (IJRSDA) 6(2), 19–33 (2019) Shawon, A., Rahman, M.J.U., Mahmud, F., Zaman, M.A.: Bangla handwritten digit recognition using deep CNN for large and unbiased dataset. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–6. IEEE, September 2018 Makkar, T., Kumar, Y., Dubey, A.K., Rocha, Á., Goyal, A.: Analogizing time complexity of KNN and CNN in recognizing handwritten digits. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6. IEEE, December 2017 Rizvi, M., Raza, H., Tahzeeb, S., Jaffry, S.: Optical character recognition based intelligent database management system for examination process control. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 500–507. IEEE, January 2019 |
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amelec, viloriaRico, ReinaldoPineda, Omar2020-11-11T22:46:13Z2020-11-11T22:46:13Z20202021-05-072194-5357https://hdl.handle.net/11323/7271Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/OCR (Optical Character Recognition) is a line of research within image processing for which many techniques and methodologies have been developed. Set of pixels recognized based on the digitalized image and this study presents an iterative process that consists of five phases of the OCR. For this purpose, several image processing methods are applied, as well as two variable selection methods, and several supervised automated learning methods are explored. Among the classification models, those of deep learning stand out for their novelty and enormous potential.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Rico, Reinaldo-will be generated-orcid-0000-0002-0730-4838-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/record/display.uri?eid=2-s2.0-85089245515&doi=10.1007%2f978-3-030-51859-2_2&origin=inward&txGid=1f85feb85a61963014477b3db2a85ca2Classification modelsGenetic algorithmImage processingMethodsRecognition of handwritten digitsRecognition of handwritten digits by image processing methods and classification modelsPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionBoufenar, C., Kerboua, A., Batouche, M.: Investigation on deep learning for off-line handwritten Arabic character recognition. Cogn. Syst. Res. 50, 180–195 (2018)Dasgupta, J., Bhattacharya, K., Chanda, B.: A holistic approach for Off-line handwritten cursive word recognition using directional feature based on Arnold transform. Pattern Recogn. Lett. 79, 73–79 (2016)Jangid, M., Srivastava, S.: Handwritten devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. J. Imaging 4(2), 41 (2018)Tarawneh, A.S., Hassanat, A.B., Chetverikov, D., Lendak, I., Verma, C.: Invoice classification using deep features and machine learning techniques. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 855–859. IEEE, April 2019Niu, X.X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012)Wang, Z., Wang, R., Gao, J., Gao, Z., Liang, Y.: Fault recognition using an ensemble classifier based on Dempster-Shafer Theory. Pattern Recogn. 99, 107079 (2020)Zhou, B., Ghose, T., Lukowicz, P.: Expressure: detect expressions related to emotional and cognitive activities using forehead textile pressure mechanomyography. Sensors 20(3), 730 (2020)Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)Mohiuddin, K., Mao, J.: A comparative study of different classifiers for handprinted character recognition. Pattern Recogn. Practice IV, 437–448 (2014)Le Cun, Y., Cortes, C.: MNIST handwritten digit database. AT&T Labs. http://yann.lecun.com/exdb/mnist. Accessed: 20 Dec 2019Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019)Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)Koresh, M.H.J.D., Deva, J.: Computer vision based traffic sign sensing for smart transport. J. Innov. Image Process. (JIIP) 1(01), 11–19 (2019)Zhang, B., Fu, M., Yan, H.: A nonlinear neural network model of mixture of local principal component analysis: application to handwritten digits recognition. Pattern Recogn. 34(2), 203–214 (2001)Ghosh, A., Pavate, A., Gholam, V., Shenoy, G., Mahadik, S.: Steady model for classification of handwritten digit recognition. In: Sharma, R., Mishra, M., Nayak, J., Naik, B., Pelusi, D. (eds.) Innovation in Electrical Power Engineering, Communication, and Computing Technology. LNEE, vol. 630, pp. 401–412. Springer, Singapore (2020). https://ezproxy.cuc.edu.co:2067/10.1007/978-981-15-2305-2_32Garg, A., Gupta, D., Saxena, S., Sahadev, P.P.: Validation of random dataset using an efficient CNN model trained on MNIST handwritten dataset. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 602–606. IEEE, March 2019El-Sawy, A., Hazem, E.B., Loey, M.: CNN for handwritten arabic digits recognition based on LeNet-5. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 566–575. Springer, Cham, October 2016Paul, O.: Image pre-processing on NumtaDB for Bengali handwritten digit recognition. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–6. IEEE, September 2018Shamsuddin, M.R., Abdul-Rahman, S., Mohamed, A.: Exploratory analysis of MNIST handwritten digit for machine learning modelling. In: International Conference on Soft Computing in Data Science, pp. 134–145. Springer, Singapore, August 2018Pujari, P., Majhi, B.: Recognition of Odia handwritten digits using gradient based feature extraction method and clonal selection algorithm. Int. J. Rough Sets Data Anal. (IJRSDA) 6(2), 19–33 (2019)Shawon, A., Rahman, M.J.U., Mahmud, F., Zaman, M.A.: Bangla handwritten digit recognition using deep CNN for large and unbiased dataset. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–6. IEEE, September 2018Makkar, T., Kumar, Y., Dubey, A.K., Rocha, Á., Goyal, A.: Analogizing time complexity of KNN and CNN in recognizing handwritten digits. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6. IEEE, December 2017Rizvi, M., Raza, H., Tahzeeb, S., Jaffry, S.: Optical character recognition based intelligent database management system for examination process control. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 500–507. IEEE, January 2019PublicationORIGINALRECOGNITION OF HANDWRITTEN DIGITS BY IMAGE PROCESSING METHODS AND CLASSIFICATION MODELS.pdfRECOGNITION OF HANDWRITTEN DIGITS BY IMAGE PROCESSING METHODS AND CLASSIFICATION MODELS.pdfapplication/pdf178862https://repositorio.cuc.edu.co/bitstreams/35e72af5-4589-4662-88ed-ce70091885c5/download901eebede791aea851a1d3253a9c28bdMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/1954a5cd-ee9a-45e1-93cb-23738e597009/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/a2fea821-51b1-44aa-a336-498ca89c749d/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILRECOGNITION OF HANDWRITTEN DIGITS BY IMAGE PROCESSING METHODS AND CLASSIFICATION MODELS.pdf.jpgRECOGNITION OF HANDWRITTEN DIGITS BY IMAGE PROCESSING METHODS AND CLASSIFICATION 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