Review of Offline Handwriting Recognition Techniques in the fields of HCR and OCR
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Hemant Khandelwal, Sakshi Gupta, Arihant Kumar Jain|
|DOI : 10.14445/22312803/IJCTT-V47P123|
Hemant Khandelwal, Sakshi Gupta, Arihant Kumar Jain "Review of Offline Handwriting Recognition Techniques in the fields of HCR and OCR". International Journal of Computer Trends and Technology (IJCTT) V47(3):161-164, May 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Recently, numerous progressions have occurred in the area of Offline Handwriting Recognition, for example, Feature Extraction Techniques, Character Recognition Techniques and so on. HCR, Handwriting Character Recognition is the ability of a framework to interpret intelligible handwritten input from sources, for example, paper records, photos and might be sensed offline by Optical Scanning and Intelligent Word Recognition. Likewise OCR, Optical Character Recognition is the mechanical or electronic transformation of pictures of typed, manually written or printed content into machine-encoded content. It is a typical technique for digitizing printed writings and utilized as a part of machine procedures, for example, Cognitive Computing, Machine Translation and so forth. It is a field of research in Pattern Recognition, Artificial Intelligence and Computer Vision. The aim of this research is to actualize the different methods of offline handwriting recognition like, Support Vector Machine, Artificial Neural Network, Hidden Markov Model etc. in the fields of HCR and OCR. In this paper, different methods are analyzed and included to enhance programmed evaluation framework`s ease of use and enhance the character recognition system for the fields of HCR and OCR.
 Beigi, H.S.M., “An overview of handwriting recognition”, Proc. The 1st Annual Conference on Technological Advancements in Developing countries, 1993. Columbia University. p. 30-46.
 Er. Neetu Bhatia, ”Optical Character Recognition Techniques: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 5, May 2014
 Ranyang Li, Hang Wang, KaifanJi, “Feature extraction and identification of handwritten characters”, 8th International Conference on Intelligent Networks and Intelligent Systems, 2015
 N. Venkata Rao, Dr.A.S.C.S.Sastary, A.S.N.Chakravarthy, KalyanChakravarthy P, “Optical Character Recognition Technique Algorithms”, Journal of Theoretical and Applied Information Technology, Vol.83. No.2, 20th January 2016
 V. S. Dhaka, Manoj Kumar, Harshit Sharma, “Character Recognition of Offline Handwritten English Scripts: A Review”, National Conference on Cloud Computing & Big Data
 Mr. Awasare Bhushan, Ms. Patil Kalyani, Ms. Jolapure Supriya, ”Handwritten Script Recognition”, IOSR Journal of Computer Engineering (IOSR-JCE)
 Sameeksha Barve, ”Optical Character Recognition Using Artificial Neural Network”, International Journal of Advanced Research in Computer Engineering & Technology, Volume 1, Issue 4, June 2012
 Te´ofilo E. de Campos, Bodla Rakesh Babu, Manik Varma, ”Character Recognition In Natural Images”
 VivekShrivastava and Navdeep Sharma, “Artificial Neural Network Based Optical Character Recognition”, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.5, October 2012
 ElieKrevat, Elliot Cuzzillo, ”Improving Off-line Handwritten Character Recognition with Hidden Markov Models”
 Eugen-DumitruTautu And Florin Leon, ”Optical Character Recognition System Using Support Vector Machines”, Gheorghe Asachi Technical University of Ia?i, Faculty of Automatic Control and Computer Engineering
 KarishmaTyagi, VedantRastogi, ”Implementation of Character Recognition using Hidden Markov Model”, International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 2, February – 2014.
 Antonio Carlos Gay Thomé, “SVM Classifiers – Concepts and Applications to Character Recognition”, Federal University of Rio de Janeiro, Brasil.
Artificial Neural Network, Hidden Markov Model, HCR, OCR, Support Vector Machine.