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ONLINE HANDWRITTEN SCRIPT RECOGNITION
Abstract:
Automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents and search for documents on the Web containing a particular script. The increase in usage of handheld devices which accept handwritten input has created a growing demand for algorithms that can efficiently analyze and retrieve handwritten data. This paper proposes a method to classify words and lines in an online handwritten document into one of the six major scripts: Arabic, Cyrillic, Devnagari, Han, Hebrew, or Roman. The classification is based on 11 different spatial and temporal features extracted from the strokes of the words. The proposed system attains an overall classification accuracy of 87.1 percent at the word level with 5-fold cross validation on a data set containing 13,379 words. The classification accuracy improves to 95 percent as the number of words in the test sample is increased to five, and to 95.5 percent for complete text lines consisting of an average of seven words.
Chapter one
Introduction
Background of the study
Statement of the problem
Purpose of the study
Significance of the study
Limitation of the study
Chapter two
Review of literature
Chapter three
Research design and methodology
Sources of data
Location of data
Method of investigation
Chapter four
Findings
Chapter five
Recommendation and conclusion
BIBLIOGRAPHY.
CHAPTER ONE
INTRODUCTION