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Reseach Article

English Scanned Document Character Recognition and Matched and Missed Matched Analysis using NN and MDA

by Pardeep Kaur, Pooja Choudhary, Varsha Sahni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 2
Year of Publication: 2015
Authors: Pardeep Kaur, Pooja Choudhary, Varsha Sahni
10.5120/ijca2015906833

Pardeep Kaur, Pooja Choudhary, Varsha Sahni . English Scanned Document Character Recognition and Matched and Missed Matched Analysis using NN and MDA. International Journal of Computer Applications. 129, 2 ( November 2015), 31-36. DOI=10.5120/ijca2015906833

@article{ 10.5120/ijca2015906833,
author = { Pardeep Kaur, Pooja Choudhary, Varsha Sahni },
title = { English Scanned Document Character Recognition and Matched and Missed Matched Analysis using NN and MDA },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 2 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number2/23046-2015906833/ },
doi = { 10.5120/ijca2015906833 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:22:21.082010+05:30
%A Pardeep Kaur
%A Pooja Choudhary
%A Varsha Sahni
%T English Scanned Document Character Recognition and Matched and Missed Matched Analysis using NN and MDA
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 2
%P 31-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the optical character recognition is used to recognize the scanned English documents by using neural network and MDA. The human mind easily read any interrupted scanned documents but it is difficult to machine. So the optical character recognition are solved this problem. The output images are not editable by capturing camera or scanned document but with the help of optical character recognition this problem easily solved. The OCR process consists of three major sub processes like pre processing, segmentation and recognition. The neural networks are playing very important role for character recognition its helps to provide high accuracy for the character.

References
  1. Pardeep kaur and Pooja Choudhary, “Review on: English Scanned Documents”, International Journal Engineering Research, Vol. 3, Issue.2, 2015.
  2. Mohammad Imrul Jubair & Prianka Banik, “An Approach to Extract Features from Document Image for Character Recognition” Global Journal of Computer Science and Technology Graphics & Vision, Volume 13 Issue 2 Version 1.0 Year 2013
  3. Kauleshwar prsad , devvrat C.Nigam,Ashmika Lakhotiya and Dheeren Umre B.I.T Durg, India “Chracter recognition using Matlab neural network tool” International journal of u and e service and technology vol 6 no1 Feb. 2013.
  4. S.K. Thilagavathy and De R. Indra Gandhi “recognition of Distorted character using Edge Detection Algorithm” International Journal of Innovative Research in computer and communication Engineering vol 1 issue 4 June 2013.
  5. Ayatullah Faruk Mohllah, Nabamita Majumder, Subhadip Basu and Mita Nasipuri “Design of an Optical Chracter Recognition System for Camera based Handheled Devices” IJCSI International Journal of computer science issue vol 8 issue 4 no 1 july 2011.
  6. Aamir khan Hasan Farooq “Principal Component Analysis Linear Discriminant Analysis Feature Extractor for pattern Recognition” IJCSI international journal of computer science issue vol 8 issue 6 no 2 nov.2011.
  7. Yusuf Perwej and Ashish Chaturvedi “ Machine Recognition of Hand written Chracter using Neural networks” Internation journal of computer application vol 14, no 2 Jan. 2011.
  8. Vivek Shrivastava and Navdeep Sharma “Artificial Neural Network based optical character recognition” Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.5, October 2012.
  9. Md Fazlul Kader1 and Kaushik Deb2 “Neural Network based English Alphanumeric recognition” International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
  10. Thomas M. Breuel, Adnan Ul-Hasan, Mayce Al Azawi and Faisal Shafait† “High-Performance OCR for Printed English and Fraktur using LSTM Networks” 2013 12th International Conference on Document Analysis and Recognition
  11. Ivan Kastelan, Sandra Kukolj, Vukota Pekovic, Vladimir Marinkovic, Zoran Marceta, “Extraction of Text on TV Screen using Optical Character Recognition”10th Jubilee International Symposium on Intelligent Systems and Informatics September 20-22, 2012
  12. Neha Sahu,R. K. Rathy PhD.,Indu Kashyap “Survey and Analysis of Devnagari Character Recognition Techniques using Neural Networks”International Journal of Computer Applications (0975 – 888)Volume 47– No.15, June 2012
  13. Parveen Kumar ,Nitin Sharma ,Arun Rana “Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)”International Journal of Computer Applications (0975 – 8887) Volume 53– No.11, September 2012.
  14. Anoop Rekha “ Offline Handwritten Gurmukhi Character and Numeral Recognition using Different Feature Sets and Classifiers - A Survey “International Journal of Engineering Research and Applications (IJERA) “Vol. 2, Issue 3, May-Jun 2012.
  15. Prof. S.P.Kosbatwar, Prof.S.K.Pathan “Pattern Association for character recognition by Back-Propagation algorithm using Neural Network approach” International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.1, February 2012.
  16. Anita Pal1 & Dayashankar Singh “Handwritten English Character Recognition Using Neural Network” International Journal of Computer Science & CommunicationVol. 1, No. 2, July-December 2010.
  17. Kai Wang, Jianming Jin, Qingren Wang “High Performance Chinese/English Mixed OCR with Character Level Language Identification” 2009 10th International Conference on Document Analysis and Recognition.
  18. Md. Abul Hasnat, S.M. Murtoza Habib, Mumit Khan “Segmentation Free Bangla OCR using HMM: Training and Recognition” 2nd International Conference on Electrical Engineering (ICEE),Khulna, Bangladesh, 2002.
  19. Sobia T. Javed, Sarmad Hussain, Ameera Maqbool, Samia Asloob, Sehrish Jamil and Huma Moin “Segmentation Free Nastalique Urdu OCR” World Academy of Science, Engineering and Technology 70 2010.
  20. Reetika Verma1 ,Rupinder Kaur2 “Efficient Technique for Chracter Recognition using neural network and surf Feature Extraction” Reetika Verma et al, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014
  21. Rapanjot Kaur, Gagangeet Singh Aujla “Review on: Enhanced Offline Signature Recognition Using Neural Network and SVM
Index Terms

Computer Science
Information Sciences

Keywords

English Character recognition pre-processing scanned documents segmentation NN feature extraction.