Handwritten Character Recognition Using Fuzzy Membership Function

2012 ◽  
Author(s):  
Sumit Saha ◽  
T. Som
Keyword(s):  
Membership Function ◽  
Character Recognition ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Handwritten Character Recognition ◽  
Handwritten Character

scholarly journals Telugu Character Recognition Using Adaptive Fuzzy Membership Functions With Adaptive Genetic Algorithm Based Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 3092-3097
Keyword(s):  
Genetic Algorithm ◽  
Membership Function ◽  
Character Recognition ◽  
Interpolation Method ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Adaptive Genetic Algorithm ◽  
Method Performance ◽  
Adaptive Fuzzy ◽  
Feed Forward Back Propagation

A novel Telugu character recognition technique is proposed in this paper where the given Telugu handwritten document is processed by normalizing the document and removing the noise. Then slant detection followed by correction process is conceded using the bilinear interpolation method to get more accurate result. Thus the de-skewed documents text lines and characters are separated by making use of Adaptive Histogram Equalization (AHE). In the next stage, the characteristics of the segmented characters are mined with the help of the zoning method. In zoning method, an adaptive fuzzy membership function will be developed by the Adaptive Genetic Algorithm (AGA). By using AGA in zoning method the characteristics are mined from the separated characters. The mined structures are applied to the Feed Forward Back Propagation Neural Network (FFBNN) for accomplishing the learning process. During testing, more number of handwritten segmented Telugu characters will be set to the FFBNN to verify whether the input character is recognized or not. Thus, the proposed method has given more accurate recognition results by using our proposed adaptive fuzzy membership function with AGA method. The proposed method performance is evaluated by getting more number of handwritten Telugu documents and compared with the GA-FFBNN and FFBNN.

scholarly journals Assamese Handwritten Character Recognition using Supervised Fuzzy Logic

2020 ◽  
Vol 8 (5) ◽  
pp. 3750-3758
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Fuzzy Membership Function ◽  
Handwritten Character Recognition ◽  
Fuzzy Classifier ◽  
Uniform Size ◽  
Handwritten Character ◽  
Mean And Variance ◽  
Distance Vector ◽  
Membership Value

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 42x32 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.

AN ANALYTICAL STUDY OF HANDWRITTEN CHARACTER RECOGNITION

2016 ◽  
Vol 2 (4) ◽  
pp. 26
Author(s):  
VOHRA UJWAL SINGH ◽  
DWIVEDI SHRI PRAKASH ◽  
MANDORIA H.L ◽  
◽  
◽  
...  
Keyword(s):  
Character Recognition ◽  
Analytical Study ◽  
Handwritten Character Recognition ◽  
Handwritten Character
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