SignboardText: Text Detection and Recognition in In-the-Wild Signboard Images Article Swipe
Tien Do
,
Thuyen Tran
,
Thua Nguyen
,
Duy-Dinh Le
,
Thanh Duc Ngo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3395374
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3395374
Scene text detection and recognition have attracted much attention in recent years because of their potential applications. Detecting and recognizing texts in images may suffer from scene complexity and text variations. Some of these problematic cases are included in popular benchmark datasets, but only to a limited extent. In this work, we investigate the problem of scene text detection and recognition in a domain with extreme challenges. We focus on in-the-wild signboard images in which text commonly appears in different fonts, sizes, artistic styles, or languages with cluttered backgrounds. We first contribute an in-the-wild signboard dataset with 79K text instances on both line-level and word-level across 2,104 scene images. We then comprehensively evaluated recent state-of-the-art (SOTA) approaches for text detection and recognition on the dataset. By doing this, we expect to realize the barriers of current state-of-the-art approaches to solving the extremely challenging issues of scene text detection and recognition, as well as their applicability in this domain. Code and dataset are available at https://github.com/aiclub-uit/SignboardText/ and IEEE DataPort.
Related Topics
Concepts
Computer science
Text detection
Benchmark (surveying)
Artificial intelligence
Domain (mathematical analysis)
Focus (optics)
Text recognition
Code (set theory)
Word (group theory)
Pattern recognition (psychology)
Feature extraction
Image (mathematics)
Natural language processing
Cartography
Geography
Linguistics
Physics
Optics
Programming language
Philosophy
Mathematical analysis
Set (abstract data type)
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3395374
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10510446.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 156
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396506223
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396506223Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3395374Digital Object Identifier
- Title
-
SignboardText: Text Detection and Recognition in In-the-Wild Signboard ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Tien Do, Thuyen Tran, Thua Nguyen, Duy-Dinh Le, Thanh Duc NgoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3395374Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10510446.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10510446.pdfDirect OA link when available
- Concepts
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Computer science, Text detection, Benchmark (surveying), Artificial intelligence, Domain (mathematical analysis), Focus (optics), Text recognition, Code (set theory), Word (group theory), Pattern recognition (psychology), Feature extraction, Image (mathematics), Natural language processing, Cartography, Geography, Linguistics, Physics, Optics, Programming language, Philosophy, Mathematical analysis, Set (abstract data type), MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
156Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_topic.display_name | Handwritten Text Recognition Techniques |
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