Evidence map›Paper›PMID 41657401›Full record

ArticleData in brief2026

A comprehensive image dataset of American Sign Language hand gestures.

Md Famidul Islam Pranto, Md Rifatul Islam, Md Ali Akbor, Nabonita Ghosh, Md Rahatun Alam, Sudipto Chaki, Md Masudul Islam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Md Famidul Islam PrantoDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Md Rifatul IslamDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Md Ali AkborDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Nabonita GhoshDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Md Rahatun AlamDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Sudipto ChakiDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Md Masudul IslamDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present ASL-HG, a comprehensive American Sign Language (ASL) image dataset designed to advance gesture recognition and assistive technologies. The collection contains 36,000 static images across 36 classes, covering the full English alphabet (A-Z) and digits (0-9). Data were captured from 10 volunteers in Mirpur, Dhaka, Bangladesh, with each participant contributing 100 samples per class, ensuring a balanced distribution across subjects, genders, and skin tones. Unlike many existing ASL datasets, ASL-HG explicitly distinguishes between the letter "O" and the digit "0″ by including the standard two-handed ASL "zero" sign used in practical alphanumeric communication. The dataset is released in two complementary forms: raw images with natural indoor and outdoor backgrounds, and a MediaPipe-processed version with hand-segmented crops and predefined 80-20 train-test splits. This design supports both custom pre-processing and immediate model training. ASL-HG is intended to serve as a benchmark resource for developing robust and fair ASL recognition systems, reducing communication barriers for deaf and speech-impaired users, and enabling broader research in gesture-based human-computer interaction.

Indexed as

American Sign LanguageASL hand gesturesAssistive technologyComputer visionGesture datasetSign language recognition

Identifiers

PMID41657401
PMCPMC12877850

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.