Abstract
Sinhala is the main language spoken by the majority of the population of Sri Lanka. There is a clear need for an optical character recognition (OCR) system for the Sinhala language. However, the language contains very similar characters, which makes it very difficult to distinguish them except on feature analysis. The character recognition rates of previous systems proposed for Sinhala character recognition are low, and so further improvement is needed. Consequently, in this paper, we propose a new Sinhala character recognition method that uses character geometry features and artificial neural network (ANN). The results of experiments conducted using various documentary images of the Sinhala language indicate that the proposed method has better character recognition performance than conventional methods.
| Original language | English |
|---|---|
| Title of host publication | Computer Vision and Graphics - International Conference, ICCVG 2016, Proceedings |
| Publisher | Springer Verlag |
| Pages | 594-603 |
| Number of pages | 10 |
| Volume | 9972 LNCS |
| ISBN (Print) | 9783319464176 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | International Conference on Computer Vision and Graphics, ICCVG 2016 - Warsaw, Poland Duration: 2016 Sep 19 → 2016 Sep 21 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9972 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | International Conference on Computer Vision and Graphics, ICCVG 2016 |
|---|---|
| Country | Poland |
| City | Warsaw |
| Period | 16/9/19 → 16/9/21 |
Fingerprint
Keywords
- Artificial neural networks
- Character geometry features
- Character recognition
- Sinhala script
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Artificial neural network based sinhala character recognition. / Premachandra, H. Waruna H; Premachandra, Chinthaka; Kimura, Tomotaka; Kawanaka, Hiroharu.
Computer Vision and Graphics - International Conference, ICCVG 2016, Proceedings. Vol. 9972 LNCS Springer Verlag, 2016. p. 594-603 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9972 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Artificial neural network based sinhala character recognition
AU - Premachandra, H. Waruna H
AU - Premachandra, Chinthaka
AU - Kimura, Tomotaka
AU - Kawanaka, Hiroharu
PY - 2016
Y1 - 2016
N2 - Sinhala is the main language spoken by the majority of the population of Sri Lanka. There is a clear need for an optical character recognition (OCR) system for the Sinhala language. However, the language contains very similar characters, which makes it very difficult to distinguish them except on feature analysis. The character recognition rates of previous systems proposed for Sinhala character recognition are low, and so further improvement is needed. Consequently, in this paper, we propose a new Sinhala character recognition method that uses character geometry features and artificial neural network (ANN). The results of experiments conducted using various documentary images of the Sinhala language indicate that the proposed method has better character recognition performance than conventional methods.
AB - Sinhala is the main language spoken by the majority of the population of Sri Lanka. There is a clear need for an optical character recognition (OCR) system for the Sinhala language. However, the language contains very similar characters, which makes it very difficult to distinguish them except on feature analysis. The character recognition rates of previous systems proposed for Sinhala character recognition are low, and so further improvement is needed. Consequently, in this paper, we propose a new Sinhala character recognition method that uses character geometry features and artificial neural network (ANN). The results of experiments conducted using various documentary images of the Sinhala language indicate that the proposed method has better character recognition performance than conventional methods.
KW - Artificial neural networks
KW - Character geometry features
KW - Character recognition
KW - Sinhala script
UR - http://www.scopus.com/inward/record.url?scp=84989855405&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84989855405&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46418-3_53
DO - 10.1007/978-3-319-46418-3_53
M3 - Conference contribution
AN - SCOPUS:84989855405
SN - 9783319464176
VL - 9972 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 594
EP - 603
BT - Computer Vision and Graphics - International Conference, ICCVG 2016, Proceedings
PB - Springer Verlag
ER -