Publication2024
A time efficient offline handwritten character recognition using convolutional extreme learning machine
- Quality Education
- Industry, Innovation and Infrastructure
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- Imaging Science Journal
- Volume
- 72
- Issue
- 6
- Pages
- 736-748
- Published
- 1 January 2024
- DOI
- 10.1080/13682199.2023.2223011
- Scopus ID
- 85162923150
- ISSN
- 13682199
- Authors
- 4
- Open access
- No
- Citations
- 5
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Develops convolutional extreme learning machine architecture for offline handwritten character recognition across Indian regional languages with 91-94% accuracy.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
4 Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
Recognition system for multiple Indian languages supports digital literacy and accessibility of educational content in regional languages.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3 Innovation, Science and Technology
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 9 label. Develops convolutional extreme learning machine architecture for offline handwritten character recognition across Indian regional languages with 91-94% accuracy.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “A time efficient offline handwritten character recognition using convolutional extreme learning machine”. Kortex.