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Updated: Jun 16, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Visual deep learning approaches for alphabetic sign language interpretation.

Joyonta Kumar Nath¹, S A Mahadi Alam², Yasir Ullah³

¹Department of Computer Science and Engineering, International Islamic University of Chittagong, Chittagong, 4000, Bangladesh.

Scientific Reports

|June 13, 2026

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This summary is machine-generated.

This study shows deep learning models accurately recognize American Sign Language (ASL) alphabets from images. ConvNeXtXLarge achieved 99.81% accuracy, highlighting transfer learning

Area of Science:

Computer Vision
Artificial Intelligence
Human-Computer Interaction

Background:

Sign language is crucial for communication for individuals with hearing and speech impairments.
Accurate sign language alphabet recognition is essential for accessible human-computer interaction.

Purpose of the Study:

To investigate visual deep learning approaches for sign language alphabet recognition from image data.
To assess the effectiveness of transfer learning-based convolutional neural networks for hand gesture feature extraction.

Main Methods:

A systematic framework was developed to evaluate deep learning models.
Several advanced convolutional neural network architectures (ConvNeXtXLarge, EfficientNet, VGG19, ResNet-50) were tested on an American Sign Language alphabet dataset.

Related Experiment Videos

Last Updated: Jun 16, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

A unified experimental protocol was used for comparative analysis.

Main Results:

ConvNeXtXLarge demonstrated the highest recognition accuracy at 99.81%.
EfficientNet, VGG19, and ResNet-50 also achieved high accuracy rates of 99.68%, 99.31%, and 97.29%, respectively.
Transfer learning significantly enhances visual representation learning for fine-grained motion recognition.

Conclusions:

Modern transfer learning strategies are highly effective for sign language alphabet recognition.
The proposed evaluation framework provides practical insights for selecting models for sign language interpretation systems.
This research contributes to advancing assistive technologies and visual language understanding.