Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
Reading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the frequency of input images at feature extraction, which slows down...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/13/4474 |
| _version_ | 1797563915596464128 |
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| author | Direselign Addis Tadesse Chuan-Ming Liu Van-Dai Ta |
| author_facet | Direselign Addis Tadesse Chuan-Ming Liu Van-Dai Ta |
| author_sort | Direselign Addis Tadesse |
| collection | DOAJ |
| description | Reading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the frequency of input images at feature extraction, which slows down the system, requires more memory, and recognizes text inaccurately. In this paper, we proposed an octave convolution (OctConv) feature extractor and a time-restricted attention encoder-decoder module for end-to-end scene text reading. The OctConv can extract features by factorizing the input image based on their frequency. It is a direct replacement of convolutions, orthogonal and complementary, for reducing redundancies and helps to boost the reading text through low memory requirements at a faster speed. In the text reading process, features are first extracted from the input image using Feature Pyramid Network (FPN) with OctConv Residual Network with depth 50 (ResNet50). Then, a Region Proposal Network (RPN) is applied to predict the location of the text area by using extracted features. Finally, a time-restricted attention encoder-decoder module is applied after the Region of Interest (RoI) pooling is performed. A bilingual real and synthetic scene text dataset is prepared for training and testing the proposed model. Additionally, well-known datasets including ICDAR2013, ICDAR2015, and Total Text are used for fine-tuning and evaluating its performance with previously proposed state-of-the-art methods. The proposed model shows promising results on both regular and irregular or curved text detection and reading tasks. |
| first_indexed | 2024-03-10T18:49:58Z |
| format | Article |
| id | doaj.art-02b43a26325749419f505555ed99b838 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:49:58Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-02b43a26325749419f505555ed99b8382023-11-20T05:11:50ZengMDPI AGApplied Sciences2076-34172020-06-011013447410.3390/app10134474Unconstrained Bilingual Scene Text Reading Using Octave as a Feature ExtractorDireselign Addis Tadesse0Chuan-Ming Liu1Van-Dai Ta2Department of Computer Science and Information Engineering, National Taipei University of Technology (Taipei Tech), Taipei 106, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology (Taipei Tech), Taipei 106, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology (Taipei Tech), Taipei 106, TaiwanReading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the frequency of input images at feature extraction, which slows down the system, requires more memory, and recognizes text inaccurately. In this paper, we proposed an octave convolution (OctConv) feature extractor and a time-restricted attention encoder-decoder module for end-to-end scene text reading. The OctConv can extract features by factorizing the input image based on their frequency. It is a direct replacement of convolutions, orthogonal and complementary, for reducing redundancies and helps to boost the reading text through low memory requirements at a faster speed. In the text reading process, features are first extracted from the input image using Feature Pyramid Network (FPN) with OctConv Residual Network with depth 50 (ResNet50). Then, a Region Proposal Network (RPN) is applied to predict the location of the text area by using extracted features. Finally, a time-restricted attention encoder-decoder module is applied after the Region of Interest (RoI) pooling is performed. A bilingual real and synthetic scene text dataset is prepared for training and testing the proposed model. Additionally, well-known datasets including ICDAR2013, ICDAR2015, and Total Text are used for fine-tuning and evaluating its performance with previously proposed state-of-the-art methods. The proposed model shows promising results on both regular and irregular or curved text detection and reading tasks.https://www.mdpi.com/2076-3417/10/13/4474octave convolutionbilingual scene text readingEthiopic scriptattention |
| spellingShingle | Direselign Addis Tadesse Chuan-Ming Liu Van-Dai Ta Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor Applied Sciences octave convolution bilingual scene text reading Ethiopic script attention |
| title | Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor |
| title_full | Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor |
| title_fullStr | Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor |
| title_full_unstemmed | Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor |
| title_short | Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor |
| title_sort | unconstrained bilingual scene text reading using octave as a feature extractor |
| topic | octave convolution bilingual scene text reading Ethiopic script attention |
| url | https://www.mdpi.com/2076-3417/10/13/4474 |
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