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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Buta, Michal Neumann, Luka Matas, Jiri |
| Copyright Year | 2015 |
| Abstract | We propose a novel easy-to-implement stroke detector based on an efficient pixel intensity comparison to surrounding pixels. Stroke-specific keypoints are efficiently detected and text fragments are subsequently extracted by local thresholding guided by keypoint properties. Classification based on effectively calculated features then eliminates non-text regions. The stroke-specific keypoints produce 2 times less region segmentations and still detect 25% more characters than the commonly exploited MSER detector and the process is 4 times faster. After a novel efficient classification step, the number of regions is reduced to 7 times less than the standard method and is still almost 3 times faster. All stages of the proposed pipeline are scale-and rotation-invariant and support a wide variety of scripts (Latin, Hebrew, Chinese, etc.) and fonts. When the proposed detector is plugged into a scene text localization and recognition pipeline, a state-of-the-art text localization accuracy is maintained whilst the processing time is significantly reduced. |
| Starting Page | 1206 |
| Ending Page | 1214 |
| File Size | 3327783 |
| Page Count | 9 |
| File Format | |
| ISSN | 23807504 |
| e-ISBN | 9781467383912 |
| DOI | 10.1109/ICCV.2015.143 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-12-07 |
| Publisher Place | Chile |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Detectors Lead Text recognition Standards Pipelines Robustness Image edge detection |
| Content Type | Text |
| Resource Type | Article |
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