Artistic multi-script identification at character level with extreme learning machine Article Swipe
Mridul Ghosh
,
Himadri Mukherjee
,
Sk Md Obaidullah
,
KC Santosh
,
Nibaran Das
,
Kaushik Roy
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2020.03.268
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2020.03.268
In this work, a novel problem, namely artistic multi-script identification at character level has been addressed. Two types of documents: real/ natural and synthetic have been used for dataset preparation. After binarizing using Otsu's global thresholding algorithm, a semi-automatic segmentation technique has been applied for character separation. Some well-known texture based features have been considered from the segmented images and further, they have been converted into lower dimensional space by applying principal component analysis. Those final feature set are classified using an Extreme Learning based classifier and performance are compared with traditional machine learning techniques and other features. Observing the inherent complexity of the multi-script character level datasets, an encouraging outcome has been obtained.
Related Topics
Concepts
Computer science
Artificial intelligence
Character (mathematics)
Extreme learning machine
Pattern recognition (psychology)
Segmentation
Classifier (UML)
Thresholding
Identification (biology)
Feature vector
Support vector machine
Feature (linguistics)
Principal component analysis
Machine learning
Artificial neural network
Image (mathematics)
Botany
Geometry
Mathematics
Philosophy
Biology
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2020.03.268
- OA Status
- diamond
- Cited By
- 9
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3016849248
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3016849248Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.procs.2020.03.268Digital Object Identifier
- Title
-
Artistic multi-script identification at character level with extreme learning machineWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Mridul Ghosh, Himadri Mukherjee, Sk Md Obaidullah, KC Santosh, Nibaran Das, Kaushik RoyList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2020.03.268Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.procs.2020.03.268Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Character (mathematics), Extreme learning machine, Pattern recognition (psychology), Segmentation, Classifier (UML), Thresholding, Identification (biology), Feature vector, Support vector machine, Feature (linguistics), Principal component analysis, Machine learning, Artificial neural network, Image (mathematics), Botany, Geometry, Mathematics, Philosophy, Biology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 2, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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