Threshold selection and identification of character regions from complex scene images
In this research work, a comprehensive algorithm for alphanumeric character detection in general complex scene images is proposed, and specifically applied to car licence-number plate. Recently a number of papers dealing with this problem have been published. They can be broadly divided into two maj...
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| Format: | Thesis |
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2010
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| Online Access: | http://hdl.handle.net/10356/38995 |
| _version_ | 1826123475092242432 |
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| author | Li, Li |
| author2 | Chutatape, Opas |
| author_facet | Chutatape, Opas Li, Li |
| author_sort | Li, Li |
| collection | NTU |
| description | In this research work, a comprehensive algorithm for alphanumeric character detection in general complex scene images is proposed, and specifically applied to car licence-number plate. Recently a number of papers dealing with this problem have been published. They can be broadly divided into two major approaches, i.e., edge-based approach in which the number-plate location is first detected, and grey-level-feature-based approach in which the character candidates are directly detected. These approaches usually need to make some assumptions or to put some constraints on the images to limit their searching work and to improve the success rate. In this thesis, we study the generalisations of the second approach by directly detect the alphanumeric characters at any location in the complex scene image with minimum image constraints. |
| first_indexed | 2024-10-01T06:05:15Z |
| format | Thesis |
| id | ntu-10356/38995 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T06:05:15Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/389952023-07-04T16:36:25Z Threshold selection and identification of character regions from complex scene images Li, Li Chutatape, Opas School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing In this research work, a comprehensive algorithm for alphanumeric character detection in general complex scene images is proposed, and specifically applied to car licence-number plate. Recently a number of papers dealing with this problem have been published. They can be broadly divided into two major approaches, i.e., edge-based approach in which the number-plate location is first detected, and grey-level-feature-based approach in which the character candidates are directly detected. These approaches usually need to make some assumptions or to put some constraints on the images to limit their searching work and to improve the success rate. In this thesis, we study the generalisations of the second approach by directly detect the alphanumeric characters at any location in the complex scene image with minimum image constraints. Master of Engineering 2010-05-21T03:39:15Z 2010-05-21T03:39:15Z 1997 1997 Thesis http://hdl.handle.net/10356/38995 NANYANG TECHNOLOGICAL UNIVERSITY 146 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Li, Li Threshold selection and identification of character regions from complex scene images |
| title | Threshold selection and identification of character regions from complex scene images |
| title_full | Threshold selection and identification of character regions from complex scene images |
| title_fullStr | Threshold selection and identification of character regions from complex scene images |
| title_full_unstemmed | Threshold selection and identification of character regions from complex scene images |
| title_short | Threshold selection and identification of character regions from complex scene images |
| title_sort | threshold selection and identification of character regions from complex scene images |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | http://hdl.handle.net/10356/38995 |
| work_keys_str_mv | AT lili thresholdselectionandidentificationofcharacterregionsfromcomplexsceneimages |