A weight learning technique for cursive handwritten text categorization with fuzzy confusion matirx

A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering Algorithm (MOCA). Optimal Clustering Algorithm (OCA) groups texts into different subject categories. Learning is conducted t...

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Bibliographic Details
Published in2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) pp. 188 - 192
Main Author Sarker, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2016
Subjects
Accuracy
Algorithm design and analysis
BP Algorithm
Clustering algorithms
F-Score
Fuzzy Confusion Matrix
Holdout Method
Instruments
Keywords
MOCA
OCA
Performance evaluation
Precision
Recall
Senior members
Text Attributes
Text categorization
Text recognition
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Summary:A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering Algorithm (MOCA). Optimal Clustering Algorithm (OCA) groups texts into different subject categories. Learning is conducted to extract the attributes along with corresponding weights for each subjects. Fuzzy confusion matrix has been used to measure several performance metrics with Holdout method. These are satisfactory. Over and above the text learning and recognition time is very less making the system efficient also.
DOI:10.1109/CIEC.2016.7513802