Historical Kannada Handwritten Character Recognition using K-Nearest Neighbour Technique (Record no. 14235)
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| 000 -LEADER | |
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20210208153053.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210208b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 13234 |
| Author | Bannigidad, Parashuram |
| 245 ## - TITLE STATEMENT | |
| Title | Historical Kannada Handwritten Character Recognition using K-Nearest Neighbour Technique |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol 5 (1), Jan-Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | STM Journals |
| Year | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 20-26p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Most of the historical Kannada handwritten documents are preserved in the manuscript preservation center and archaeological department, and these documents are generally degraded in nature and it is very difficult to read and understand the contents in it. Hence, it is very much essential to digitize the historical Kannada handwritten document and recognize its originality of dynasty. The main objective of this paper is to digitize and recognize the historical Kannada handwritten documents by applying bonding box segmentation method and extracting the geometrical shape features, classification is performed by using K-nearest neighbor classifier. The average classification accuracy of the historical Kannada handwritten character from the different dynasties based on their age-type is: Kadamba 97.83%, Badami chalukya 97.78%, Kalyana chalukya 97.92%, Hoysala 97.87%, Vijayanagara 100%, Mysore wodeyars 97.87% and Aadhunika Kannada 97.96%. The results are compared with manual results obtained by the epigraphists and language experts, which demonstrate the efficacy of the proposed method. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 13235 |
| Co-Author | Gudada, Chandrashekar |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Noida STM Journals |
| Title | Journal of artificial intelligence research and advances (JoAIRA) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | http://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=1532 |
| Link text | Click Here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 08/02/2021 | 2021-2021464 | 08/02/2021 | 08/02/2021 | Articles Abstract Database |