Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network Article Swipe
Mridul Ghosh
,
Sayan Saha Roy
,
Himadri Mukherjee
,
Sk Md Obaidullah
,
Xiao‐Zhi Gao
,
Kaushik Roy
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3110858
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3110858
Graphical texts in natural images play an important role in portraying information in multitudinous fields such as communication, education, and entertainment to name a few. Recognizing text in scene images is challenging due to the inherent complexity of the images. Text recognition in natural images involves script identification, which requires text localization. This is not trivial for natural scene images due to the presence of disparate foreground/background components. For scene images like movie posters, the challenge is more dominant. The challenges aggravate due to the presence of composite characteristics of posters like complex graphics background and the presence of different texts like a movie title, names of actors, producers, directors, and tagline. These texts have miscellaneous fonts, variations in colors, size, orientation, and textures. In this work, an M-EAST (modified EAST) model is proposed, which is based on the EAST (efficient and accurate scene text detector) model for text localization. A novel movie title extraction is thereafter used for separating the title from the extracted text pool. Finally, the title script was identified using a shallow convolutional neural network (SCNN)-based architecture to ensure functionality in low-resource environments. Experiments were performed on a dataset of movie-poster images of Tollywood, Bollywood, and Hollywood industries, and a highest accuracy of 99.82% was obtained. The system performed better than the reported techniques.
Related Topics
Concepts
Computer science
Convolutional neural network
Artificial intelligence
Text detection
Identification (biology)
Entertainment
Graphics
Hollywood
Convolution (computer science)
Feature extraction
Computer vision
Natural language processing
Natural (archaeology)
Orientation (vector space)
Artificial neural network
Pattern recognition (psychology)
Image (mathematics)
Computer graphics (images)
Visual arts
Art
Biology
Archaeology
History
Geometry
Mathematics
Botany
Art history
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3110858
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09530511.pdf
- OA Status
- gold
- Cited By
- 19
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198300906
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3198300906Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3110858Digital Object Identifier
- Title
-
Movie Title Extraction and Script Separation Using Shallow Convolution Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Mridul Ghosh, Sayan Saha Roy, Himadri Mukherjee, Sk Md Obaidullah, Xiao‐Zhi Gao, Kaushik RoyList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3110858Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09530511.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09530511.pdfDirect OA link when available
- Concepts
-
Computer science, Convolutional neural network, Artificial intelligence, Text detection, Identification (biology), Entertainment, Graphics, Hollywood, Convolution (computer science), Feature extraction, Computer vision, Natural language processing, Natural (archaeology), Orientation (vector space), Artificial neural network, Pattern recognition (psychology), Image (mathematics), Computer graphics (images), Visual arts, Art, Biology, Archaeology, History, Geometry, Mathematics, Botany, Art historyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 1, 2023: 10, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
71Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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