Abstract
A brief description of statistical and syntactic pattern matching techniques is presented with an emphasis on statistical techniques. The characteristics of the Telugu script are described. A subset of 16 characters, which are both easy and hard to recognize, is selected for the dictionary of standard characters. A weighted linear difference polynomial of features is used to recognize Telugu characters. The features were Fourier descriptors of projection profiles and cross sections taken in various directions. Algorithms for obtaining the projection profiles cross sections and adaptive learning method are presented. The system was trained and tested with a set of 8 nano-written samples of each of 16 different Telugu characters. More than 90% of the 123 samples were correctly recognized by the system. Results of numerous trials examining the different features and classification techniques are discussed.
Library of Congress Subject Headings
Optical pattern recognition; Pattern recognition systems; Telugu language--Data processing
Publication Date
1987
Document Type
Thesis
Student Type
Graduate
Degree Name
Computer Science (MS)
Department, Program, or Center
Computer Science (GCCIS)
Advisor
John A. Biles
Advisor/Committee Member
Larry Coon
Advisor/Committee Member
Peter Anderson
Recommended Citation
Mantha, Murthy Lakshmana, "Adaptive statistical recognition of hand-printed Telugu characters" (1987). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/8400
Campus
RIT – Main Campus
Comments
Physical copy available from RIT's Wallace Library at TA1650.M36 1987