Character/Word Modelling: A Two-Step Framework for Text Recognition in Natural Scene Images Article Swipe
M. Priya
,
A. Pavithra
,
Leema Nelson
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7494/csci.2024.25.4.6164
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7494/csci.2024.25.4.6164
Text recognition from images is a complex task in computer vision. Traditional text recognition methods typically rely on Optical Character Recognition (OCR); however, their limitations in image processing can lead to unreliable results. However, recent advancements in deep-learning models have provided an effective alternative for recognizing and classifying text in images. This study proposes a deep-learning-based text recognition system for natural scene images that incorporates character/word modeling, a two-step procedure involving the recognition of characters and words. In the first step, Convolutional Neural Networks (CNN) are used to differentiate individual characters from image frames. In the second step, the Viterbi search algorithm employs lexicon-based word recognition to determine the optimal sequence of recognized characters, thereby enabling accurate word identification in natural scene images. The system is tested using the ICDAR 2003 and ICDAR 2013 datasets from the Kaggle repository, and achieved accuracies of 79.8% and 81.5%, respectively.
Related Topics
Concepts
Computer science
Artificial intelligence
Convolutional neural network
Optical character recognition
Character (mathematics)
Word (group theory)
Pattern recognition (psychology)
Natural language processing
Word recognition
Deep learning
Intelligent word recognition
Image (mathematics)
Lexicon
Speech recognition
Character recognition
Intelligent character recognition
Philosophy
Political science
Geometry
Reading (process)
Mathematics
Linguistics
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7494/csci.2024.25.4.6164
- https://journals.agh.edu.pl/csci/article/download/6164/3100
- OA Status
- diamond
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405893256
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405893256Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7494/csci.2024.25.4.6164Digital Object Identifier
- Title
-
Character/Word Modelling: A Two-Step Framework for Text Recognition in Natural Scene ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-30Full publication date if available
- Authors
-
M. Priya, A. Pavithra, Leema NelsonList of authors in order
- Landing page
-
https://doi.org/10.7494/csci.2024.25.4.6164Publisher landing page
- PDF URL
-
https://journals.agh.edu.pl/csci/article/download/6164/3100Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://journals.agh.edu.pl/csci/article/download/6164/3100Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Convolutional neural network, Optical character recognition, Character (mathematics), Word (group theory), Pattern recognition (psychology), Natural language processing, Word recognition, Deep learning, Intelligent word recognition, Image (mathematics), Lexicon, Speech recognition, Character recognition, Intelligent character recognition, Philosophy, Political science, Geometry, Reading (process), Mathematics, Linguistics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2053317383, https://openalex.org/W3024790198, https://openalex.org/W3104991093, https://openalex.org/W2170727726, https://openalex.org/W2994189733, https://openalex.org/W2076014259, https://openalex.org/W2106693967, https://openalex.org/W1975453746, https://openalex.org/W1922126009, https://openalex.org/W2963233387, https://openalex.org/W2609829154, https://openalex.org/W2404161323, https://openalex.org/W6604253536, https://openalex.org/W4241103812, https://openalex.org/W2791286046, https://openalex.org/W2194187530, https://openalex.org/W2043011136, https://openalex.org/W1998042868, https://openalex.org/W2767052698, https://openalex.org/W4319777850, https://openalex.org/W2053124390, https://openalex.org/W2339589954, https://openalex.org/W2899996070, https://openalex.org/W2279370144, https://openalex.org/W1569614731, https://openalex.org/W3136276185, https://openalex.org/W2949847672 |
| referenced_works_count | 27 |
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