Online isolated handwriting and text recognition based on annotated image features
The representation schemes of input pattern and model database are of particular importance since a classification method depends largely on them (Liu et al ., 2004). Selecting the data representation is one of the most fundamental decisions to make (Jong, 2001). This chapter...
| Main Authors: | , |
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| Format: | Book Section |
| Language: | English |
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Penerbit UTM
2008
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| Subjects: | |
| Online Access: | http://eprints.utm.my/24949/1/bookchapter_fsksm062.pdf |
| _version_ | 1796856045668139008 |
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| author | Zafar, Muhammad Faisal Mohamad, Dzulkifli |
| author_facet | Zafar, Muhammad Faisal Mohamad, Dzulkifli |
| author_sort | Zafar, Muhammad Faisal |
| collection | ePrints |
| description | The representation schemes of input pattern and model database are of particular importance since a classification method depends largely on them (Liu et al ., 2004). Selecting the data representation is one of the most fundamental decisions to make (Jong, 2001). This chapter describes the simple techniques involved in extracting the annotated image features from online handwriting as well as printed isolated English alphabets and their representation in a standard form to be used by the recognition stage. Conventionally, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, here we present an easy approach to extract the useful character information. Here, the neural network approaches have been used for a writer-independent recognition system. |
| first_indexed | 2024-03-05T18:37:36Z |
| format | Book Section |
| id | utm.eprints-24949 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:37:36Z |
| publishDate | 2008 |
| publisher | Penerbit UTM |
| record_format | dspace |
| spelling | utm.eprints-249492017-10-10T00:56:01Z http://eprints.utm.my/24949/ Online isolated handwriting and text recognition based on annotated image features Zafar, Muhammad Faisal Mohamad, Dzulkifli QA75 Electronic computers. Computer science The representation schemes of input pattern and model database are of particular importance since a classification method depends largely on them (Liu et al ., 2004). Selecting the data representation is one of the most fundamental decisions to make (Jong, 2001). This chapter describes the simple techniques involved in extracting the annotated image features from online handwriting as well as printed isolated English alphabets and their representation in a standard form to be used by the recognition stage. Conventionally, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, here we present an easy approach to extract the useful character information. Here, the neural network approaches have been used for a writer-independent recognition system. Penerbit UTM 2008 Book Section PeerReviewed application/pdf en http://eprints.utm.my/24949/1/bookchapter_fsksm062.pdf Zafar, Muhammad Faisal and Mohamad, Dzulkifli (2008) Online isolated handwriting and text recognition based on annotated image features. In: Advances in Image Processing and Pattern Recognition: Algorithms & Practice, Vol. II. Penerbit UTM , Johor, pp. 1-36. ISBN 978-983-52-0618-4 |
| spellingShingle | QA75 Electronic computers. Computer science Zafar, Muhammad Faisal Mohamad, Dzulkifli Online isolated handwriting and text recognition based on annotated image features |
| title | Online isolated handwriting and text recognition based on annotated image features |
| title_full | Online isolated handwriting and text recognition based on annotated image features |
| title_fullStr | Online isolated handwriting and text recognition based on annotated image features |
| title_full_unstemmed | Online isolated handwriting and text recognition based on annotated image features |
| title_short | Online isolated handwriting and text recognition based on annotated image features |
| title_sort | online isolated handwriting and text recognition based on annotated image features |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/24949/1/bookchapter_fsksm062.pdf |
| work_keys_str_mv | AT zafarmuhammadfaisal onlineisolatedhandwritingandtextrecognitionbasedonannotatedimagefeatures AT mohamaddzulkifli onlineisolatedhandwritingandtextrecognitionbasedonannotatedimagefeatures |