On developing handwritten character image database for Malayalam language script
The objective of this paper is to build a handwritten character image database for Malayalam language script. Standard handwritten document image databases are an essential requirement for the development and objective evaluation of different handwritten text recognition systems for any language scr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2019-04-01
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| Series: | Engineering Science and Technology, an International Journal |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098618301447 |
| _version_ | 1818313829199642624 |
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| author | K. Manjusha M. Anand Kumar K.P. Soman |
| author_facet | K. Manjusha M. Anand Kumar K.P. Soman |
| author_sort | K. Manjusha |
| collection | DOAJ |
| description | The objective of this paper is to build a handwritten character image database for Malayalam language script. Standard handwritten document image databases are an essential requirement for the development and objective evaluation of different handwritten text recognition systems for any language script. Considerable research efforts for handwritten Malayalam character recognition are present in literature. Still, no public domain handwritten image database is available for the Malayalam language. The present work focuses on building an open source handwritten character image database for Malayalam language script. The unique orthographic representation of the Malayalam characters forms the different character classes, and the current version of the database contains 85 character classes frequently used in writing Malayalam text. Handwritten data samples collected from 77 native Malayalam writers. For extracting the character images from the handwritten data sheets, active contour model-based image segmentation algorithm utilized. Recognition experiments conducted on the created character image database by employing different feature extraction techniques. Among the considered feature descriptors, scattering convolutional network-based feature descriptors attain the highest recognition accuracy of 91.05%. Keywords: Malayalam language, Handwritten character recognition, Handwritten character image database, Active contour minimization, Optical character recognition, Feature extraction |
| first_indexed | 2024-12-13T08:39:58Z |
| format | Article |
| id | doaj.art-ae77de9777ce45bebc3178d561e7eece |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-12-13T08:39:58Z |
| publishDate | 2019-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-ae77de9777ce45bebc3178d561e7eece2022-12-21T23:53:33ZengElsevierEngineering Science and Technology, an International Journal2215-09862019-04-01222637645On developing handwritten character image database for Malayalam language scriptK. Manjusha0M. Anand Kumar1K.P. Soman2Center for Computational Engineering & Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India; Corresponding author.Department of Information Technology, NIT K - Surathkal, Mangalore 575025, IndiaCenter for Computational Engineering & Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, IndiaThe objective of this paper is to build a handwritten character image database for Malayalam language script. Standard handwritten document image databases are an essential requirement for the development and objective evaluation of different handwritten text recognition systems for any language script. Considerable research efforts for handwritten Malayalam character recognition are present in literature. Still, no public domain handwritten image database is available for the Malayalam language. The present work focuses on building an open source handwritten character image database for Malayalam language script. The unique orthographic representation of the Malayalam characters forms the different character classes, and the current version of the database contains 85 character classes frequently used in writing Malayalam text. Handwritten data samples collected from 77 native Malayalam writers. For extracting the character images from the handwritten data sheets, active contour model-based image segmentation algorithm utilized. Recognition experiments conducted on the created character image database by employing different feature extraction techniques. Among the considered feature descriptors, scattering convolutional network-based feature descriptors attain the highest recognition accuracy of 91.05%. Keywords: Malayalam language, Handwritten character recognition, Handwritten character image database, Active contour minimization, Optical character recognition, Feature extractionhttp://www.sciencedirect.com/science/article/pii/S2215098618301447 |
| spellingShingle | K. Manjusha M. Anand Kumar K.P. Soman On developing handwritten character image database for Malayalam language script Engineering Science and Technology, an International Journal |
| title | On developing handwritten character image database for Malayalam language script |
| title_full | On developing handwritten character image database for Malayalam language script |
| title_fullStr | On developing handwritten character image database for Malayalam language script |
| title_full_unstemmed | On developing handwritten character image database for Malayalam language script |
| title_short | On developing handwritten character image database for Malayalam language script |
| title_sort | on developing handwritten character image database for malayalam language script |
| url | http://www.sciencedirect.com/science/article/pii/S2215098618301447 |
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