ArticleSensors (Basel, Switzerland)2021
American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed, 107 citations in OpenAlex.
- Recognizing American Sign Language gestures efficiently and accurately using a hybrid transformer model.Scientific reports · 2025Article
- Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets.Journal of imaging · 2025Article
- Innovative hand pose based sign language recognition using hybrid metaheuristic optimization algorithms with deep learning model for hearing impaired persons.Scientific reports · 2025Article
- A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.Sensors (Basel, Switzerland) · 2024Article
- Sign language recognition based on dual-path background erasure convolutional neural network.Scientific reports · 2024Article
- Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model.Sensors (Basel, Switzerland) · 2024Article
- Evaluating the Reliability of Three-dimensional Models Constructed Photogrammetry Software 3DF Zephyr by Measuring Joint Angles of Fingers: A Comparison to a Conventional Goniometer.Journal of plastic and reconstructive surgery · 2024Article
- Dynamic Japanese Sign Language Recognition Throw Hand Pose Estimation Using Effective Feature Extraction and Classification Approach.Sensors (Basel, Switzerland) · 2024Article
- A Computational Approach to Hand Pose Recognition in Early Modern Paintings.Journal of imaging · 2023Article
- Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace.Sensors (Basel, Switzerland) · 2022Article
- Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns.Sensors (Basel, Switzerland) · 2022Article
- American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM.Sensors (Basel, Switzerland) · 2022Article
- Validation of two-dimensional video-based inference of finger kinematics with pose estimation.PloS one · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due to the cost-effectiveness of vision-based approaches, researchers have been conducted here also despite the accuracy drop. The purpose of this research is to recognize American sign characters using hand images obtained from a web camera. In this work, the media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: one is the distances between the joint points and the other one is the angles between vectors and 3D axes. The classifiers utilized to classify the characters were support vector machine (SVM) and light gradient boosting machine (GBM). Three character datasets were used for recognition: the ASL Alphabet dataset, the Massey dataset, and the finger spelling A dataset. The results obtained were 99.39% for the Massey dataset, 87.60% for the ASL Alphabet dataset, and 98.45% for Finger Spelling A dataset. The proposed design for automatic American sign language recognition is cost-effective, computationally inexpensive, does not require any special sensors or devices, and has outperformed previous studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.