Recognition of cursive Arabic handwritten text using embedded training based on HMMs
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-09-01
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| Series: | Journal of Electrical Systems and Information Technology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2314717217300156 |
| _version_ | 1819295127549509632 |
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| author | Rabi Mouhcine Amrouch Mustapha Mahani Zouhir |
| author_facet | Rabi Mouhcine Amrouch Mustapha Mahani Zouhir |
| author_sort | Rabi Mouhcine |
| collection | DOAJ |
| description | In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition. Keywords: Recognition, Handwriting, Arabic text, HMMs, Embedded training |
| first_indexed | 2024-12-24T04:37:17Z |
| format | Article |
| id | doaj.art-dd727baf651746298e00f2d53c0ae4ca |
| institution | Directory Open Access Journal |
| issn | 2314-7172 |
| language | English |
| last_indexed | 2024-12-24T04:37:17Z |
| publishDate | 2018-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj.art-dd727baf651746298e00f2d53c0ae4ca2022-12-21T17:15:05ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722018-09-0152245251Recognition of cursive Arabic handwritten text using embedded training based on HMMsRabi Mouhcine0Amrouch Mustapha1Mahani Zouhir2Laboratory IRF-SIC, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco; Corresponding author.Laboratory IRF-SIC, Faculty of Sciences, Ibn Zohr University, Agadir, MoroccoHight School of Technology, Ibn Zohr University, Agadir, MoroccoIn this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition. Keywords: Recognition, Handwriting, Arabic text, HMMs, Embedded traininghttp://www.sciencedirect.com/science/article/pii/S2314717217300156 |
| spellingShingle | Rabi Mouhcine Amrouch Mustapha Mahani Zouhir Recognition of cursive Arabic handwritten text using embedded training based on HMMs Journal of Electrical Systems and Information Technology |
| title | Recognition of cursive Arabic handwritten text using embedded training based on HMMs |
| title_full | Recognition of cursive Arabic handwritten text using embedded training based on HMMs |
| title_fullStr | Recognition of cursive Arabic handwritten text using embedded training based on HMMs |
| title_full_unstemmed | Recognition of cursive Arabic handwritten text using embedded training based on HMMs |
| title_short | Recognition of cursive Arabic handwritten text using embedded training based on HMMs |
| title_sort | recognition of cursive arabic handwritten text using embedded training based on hmms |
| url | http://www.sciencedirect.com/science/article/pii/S2314717217300156 |
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