Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary
Text information contained in scene images is very helpful for high‐level image understanding. In this study, the authors propose to learn co‐occurrence of local strokes for scene text recognition by using a spatiality embedded dictionary (SED). Unlike spatial pyramid partitioning images into grids...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-02-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2014.0022 |
| _version_ | 1797684788901969920 |
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| author | Song Gao Chunheng Wang Baihua Xiao Cunzhao Shi Wen Zhou Zhong Zhang |
| author_facet | Song Gao Chunheng Wang Baihua Xiao Cunzhao Shi Wen Zhou Zhong Zhang |
| author_sort | Song Gao |
| collection | DOAJ |
| description | Text information contained in scene images is very helpful for high‐level image understanding. In this study, the authors propose to learn co‐occurrence of local strokes for scene text recognition by using a spatiality embedded dictionary (SED). Unlike spatial pyramid partitioning images into grids to incorporate spatial information, the authors SED associates every codeword with a particular response region and introduces more precise spatial information for robust character recognition. After localised soft coding and max pooling of the first layer, a sparse dictionary is learned to model co‐occurrence of several local strokes, which further improves classification performance. Experimental results on two scene character recognition datasets ICDAR2003 and CHARS74 K demonstrate that their character recognition method outperforms state‐of‐the‐art methods. Besides, competitive word recognition results are also reported for four benchmark word recognition datasets ICDAR2003, ICDAR2011, ICDAR2013 and street view text when combining their character recognition method with a conditional random field language model. |
| first_indexed | 2024-03-12T00:34:52Z |
| format | Article |
| id | doaj.art-204b50dc13e6437d85789050cb1b1cb7 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:34:52Z |
| publishDate | 2015-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-204b50dc13e6437d85789050cb1b1cb72023-09-15T09:38:37ZengWileyIET Computer Vision1751-96321751-96402015-02-019113814810.1049/iet-cvi.2014.0022Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionarySong Gao0Chunheng Wang1Baihua Xiao2Cunzhao Shi3Wen Zhou4Zhong Zhang5The State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of SciencesBeijingPeople's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of SciencesBeijingPeople's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of SciencesBeijingPeople's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of SciencesBeijingPeople's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of SciencesBeijingPeople's Republic of ChinaThe State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of SciencesBeijingPeople's Republic of ChinaText information contained in scene images is very helpful for high‐level image understanding. In this study, the authors propose to learn co‐occurrence of local strokes for scene text recognition by using a spatiality embedded dictionary (SED). Unlike spatial pyramid partitioning images into grids to incorporate spatial information, the authors SED associates every codeword with a particular response region and introduces more precise spatial information for robust character recognition. After localised soft coding and max pooling of the first layer, a sparse dictionary is learned to model co‐occurrence of several local strokes, which further improves classification performance. Experimental results on two scene character recognition datasets ICDAR2003 and CHARS74 K demonstrate that their character recognition method outperforms state‐of‐the‐art methods. Besides, competitive word recognition results are also reported for four benchmark word recognition datasets ICDAR2003, ICDAR2011, ICDAR2013 and street view text when combining their character recognition method with a conditional random field language model.https://doi.org/10.1049/iet-cvi.2014.0022scene text recognitionhigh-level image understandingtext informationscene imageslocal strokesspatiality embedded dictionary |
| spellingShingle | Song Gao Chunheng Wang Baihua Xiao Cunzhao Shi Wen Zhou Zhong Zhang Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary IET Computer Vision scene text recognition high-level image understanding text information scene images local strokes spatiality embedded dictionary |
| title | Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary |
| title_full | Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary |
| title_fullStr | Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary |
| title_full_unstemmed | Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary |
| title_short | Scene text recognition by learning co‐occurrence of strokes based on spatiality embedded dictionary |
| title_sort | scene text recognition by learning co occurrence of strokes based on spatiality embedded dictionary |
| topic | scene text recognition high-level image understanding text information scene images local strokes spatiality embedded dictionary |
| url | https://doi.org/10.1049/iet-cvi.2014.0022 |
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