Mobile application on a scene text spotting
Scene text detection methods in computer vision and object detection relying heavily on neural network and deep learning have emerged recently, showing promising results. The topic of object recognition is a subject of ongoing active research and have been used in applications such as text localizat...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138067 |
| _version_ | 1811676562227986432 |
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| author | Chua, Kah Yong |
| author2 | Loke Yuan Ren |
| author_facet | Loke Yuan Ren Chua, Kah Yong |
| author_sort | Chua, Kah Yong |
| collection | NTU |
| description | Scene text detection methods in computer vision and object detection relying heavily on neural network and deep learning have emerged recently, showing promising results. The topic of object recognition is a subject of ongoing active research and have been used in applications such as text localization, surveillance, aerial imaging and autonomous driving. In addition, applications of scene text detection include multilingual text translation on mobile phone aiding users with instant translation, blind-navigation and image information retrieval.While recent advancement in the field have led to improved accuracies, precisions and f-measures, text extraction from natural scenes still pose as a challenging problem often involve with complex issues. These problems include, dis-oriented text, perspective distortion, arbitrary shaped text, variation in text sizes, uneven lighting and blurring. State-of-the-art object detector localize text of interest accurately by drawing horizontal/vertical, rectangular shaped bounding boxes over an object. These methods fail to address the issue of perspective distortion, variation in text sizes and arbitrary shaped text resulting in reduced text localization and limiting downstream task. To overcome these limitations, this research paper introduces two solutions that addresses these issues to achieve more precise detections producing better fitted and tighter bounding boxes. Specifically,by adding a new parameter call angle in the anchor box parameter definition and predict a rotation of clockwise or anti-clockwise around it’s midpoint and by allowing the anchor boxes to morph around it four point definitions. As a result, further improving the text localization network accuracy with minimum impact in its reliability and speed. |
| first_indexed | 2024-10-01T02:23:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138067 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:23:27Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1380672020-04-23T04:18:34Z Mobile application on a scene text spotting Chua, Kah Yong Loke Yuan Ren School of Computer Science and Engineering yrloke@ntu.edu.sg Engineering::Computer science and engineering Scene text detection methods in computer vision and object detection relying heavily on neural network and deep learning have emerged recently, showing promising results. The topic of object recognition is a subject of ongoing active research and have been used in applications such as text localization, surveillance, aerial imaging and autonomous driving. In addition, applications of scene text detection include multilingual text translation on mobile phone aiding users with instant translation, blind-navigation and image information retrieval.While recent advancement in the field have led to improved accuracies, precisions and f-measures, text extraction from natural scenes still pose as a challenging problem often involve with complex issues. These problems include, dis-oriented text, perspective distortion, arbitrary shaped text, variation in text sizes, uneven lighting and blurring. State-of-the-art object detector localize text of interest accurately by drawing horizontal/vertical, rectangular shaped bounding boxes over an object. These methods fail to address the issue of perspective distortion, variation in text sizes and arbitrary shaped text resulting in reduced text localization and limiting downstream task. To overcome these limitations, this research paper introduces two solutions that addresses these issues to achieve more precise detections producing better fitted and tighter bounding boxes. Specifically,by adding a new parameter call angle in the anchor box parameter definition and predict a rotation of clockwise or anti-clockwise around it’s midpoint and by allowing the anchor boxes to morph around it four point definitions. As a result, further improving the text localization network accuracy with minimum impact in its reliability and speed. Bachelor of Engineering (Computer Engineering) 2020-04-23T04:18:34Z 2020-04-23T04:18:34Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138067 en SCSE19-0105 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Chua, Kah Yong Mobile application on a scene text spotting |
| title | Mobile application on a scene text spotting |
| title_full | Mobile application on a scene text spotting |
| title_fullStr | Mobile application on a scene text spotting |
| title_full_unstemmed | Mobile application on a scene text spotting |
| title_short | Mobile application on a scene text spotting |
| title_sort | mobile application on a scene text spotting |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/138067 |
| work_keys_str_mv | AT chuakahyong mobileapplicationonascenetextspotting |