Input Fuzzy Modeling for the recognition of handwritten Hindi numerals
Hanmandlu, Madasu, Grover, Jyotsana, Madasu, Vamsi K., & Vasikarla, Shantaram (2007) Input Fuzzy Modeling for the recognition of handwritten Hindi numerals. In Latifi, Shahram (Ed.) 4th International Conference on Information Technology, 2007. ITNG '07, 2-4 April 2007, Las Vegas, USA.
This paper presents the recognition of Handwritten Hindi Numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance features obtained using the Box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing the criterion function associated with the input fuzzy modeling. We then utilize a 'Reuse Policy' that provides guidance from past error values of the criteria function to accomplish the reinforcement learning. We will also show how the 'Reuse Policy' improves the speed of convergence of the learning process over other strategies that learn without reuse. There is a 25-fold improvement in training with the use of the reinforcement learning Experimentation is carried out on a limited database of around 3500 Hindi numeral samples. The overall recognition rate is found to be 95%.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Input, Output fuzzy modelling, Hindi character recognition, Reinforcement Learning, Membership functions, Structural parameters|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||29 Feb 2008|
|Last Modified:||10 Aug 2011 18:03|
Repository Staff Only: item control page