Assamese Handwritten Character Recognition using Supervised Fuzzy Logic
Kalyanbrat Medhi1, Sanjib Kr. Kalita2
1Kalyanbrat Medhi*, Computer Science, Gauhati University, Guwahati, India.
2Sanjib Kr. Kalita, Computer Science, Gauhati University, Guwahati, India.

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3750-3758 | Volume-8 Issue-5, January 2020. | Retrieval Number: D9989118419/2020©BEIESP | DOI: 10.35940/ijrte.D9989.018520

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Abstract: This paper presents a state of the art supervised fuzzy pattern recognition system for recognition of Assamese handwritten characters. The fuzzy classifier is well suited for applications with ambiguities and handwritten character recognition is such a task. The dataset used in this experiment is taken from ISI Kolkata. After preprocessing images are normalized into uniform size 42×32 and then two features namely distance vector and density vector have been extracted. The experiment has two stages, training and testing. In first stage we extract distance vector and density features from uniform zones of the binary images for training classes and estimate the mean and variance for each class. In second stage we use this mean and variance to calculate the membership values for each unknown character of the testing set of data. An exponential fuzzy membership function is used for this purpose. Finally we recognize an unknown test character as that class for which it gives highest membership value. Finally result is stored in editable document. The highest recognition accuracy achieved in the experiment is 88.29%, 86.55% and 82.74% for numerals, vowels and consonants respectively.
Keywords: Assamese Handwritten Character, Fuzzy Logic, Fuzzy Membership Function, Pattern Recognition, Distance Vector, Density Vector, Uniform Zoning.
Scope of the Article: Fuzzy Logic.