TY - GEN
T1 - Text line script identification for a tri-lingual document
AU - Aithal, Prakash K.
AU - Rajesh, G.
AU - Acharya, Dinesh U.
AU - Subbareddy, Krishnamoorthi M.N.V.
PY - 2010
Y1 - 2010
N2 - India is a multilingual multi-script country. States of India follow a three language formula. The document may be printed in English, Hindi and other state official language. For example in Karnataka, a state in India, the document may contain text lines in English, Hindi script. For Optical Character Recognition (OCR) of such a multilingual document, it is necessary to identify the script before feeding the text lines to the OCRs of individual scripts. In this paper, a simple and efficient technique of script identification for Kannada, Hindi and English text lines from a printed document is presented. The proposed system uses horizontal projection profile to distinguish the three scripts. The feature extraction is done based on the horizontal projection profile of each text line. The knowledge base of the system is developed based on 15 different document images containing about 450 text lines. For a new text line, necessary features are extracted from the horizontal projection profile and compared with the stored knowledge base to classify the script. The proposed system is tested on 20 different document images containing about 200 text lines of each script and an overall classification rate of 99.83% is achieved.
AB - India is a multilingual multi-script country. States of India follow a three language formula. The document may be printed in English, Hindi and other state official language. For example in Karnataka, a state in India, the document may contain text lines in English, Hindi script. For Optical Character Recognition (OCR) of such a multilingual document, it is necessary to identify the script before feeding the text lines to the OCRs of individual scripts. In this paper, a simple and efficient technique of script identification for Kannada, Hindi and English text lines from a printed document is presented. The proposed system uses horizontal projection profile to distinguish the three scripts. The feature extraction is done based on the horizontal projection profile of each text line. The knowledge base of the system is developed based on 15 different document images containing about 450 text lines. For a new text line, necessary features are extracted from the horizontal projection profile and compared with the stored knowledge base to classify the script. The proposed system is tested on 20 different document images containing about 200 text lines of each script and an overall classification rate of 99.83% is achieved.
UR - https://www.scopus.com/pages/publications/78549264084
UR - https://www.scopus.com/inward/citedby.url?scp=78549264084&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT.2010.5592562
DO - 10.1109/ICCCNT.2010.5592562
M3 - Conference contribution
AN - SCOPUS:78549264084
SN - 9781424465910
T3 - 2010 2nd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2010
BT - 2010 2nd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2010
T2 - 2010 2nd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2010
Y2 - 29 July 2010 through 31 July 2010
ER -