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Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

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Scene Uyghur Recognition Based on Visual Prediction Enhancement.

Yaqi Liu^1,2,3, Fanjie Kong^1,2,3, Miaomiao Xu^1,2,3

¹College of Information Science and Engineering, Xinjang University, No. 777 Huarui Street, Urumqi 830017, China.

Sensors (Basel, Switzerland)

|October 28, 2023

Summary

This study introduces an improved scene Uyghur recognition model to address text deformation and character similarity issues. The enhanced model achieves more accurate visual predictions for Uyghur text in complex scene images.

Keywords:

Uyghur recognition correction network scene Uyghur dataset scene text recognition vision model

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Area of Science:

Computer Vision
Natural Language Processing
Optical Character Recognition

Background:

Scene text recognition faces challenges with Uyghur text, including oblique deformation, character adhesion, and similarity.
Existing methods struggle to accurately recognize Uyghur characters in diverse and complex image conditions.

Purpose of the Study:

To develop an enhanced scene Uyghur recognition model with improved visual prediction capabilities.
To address specific issues of skewed text, character adhesion, and similar character differentiation in Uyghur scene text.

Main Methods:

Utilized TPS++ for feature-level correction of skewed text.
Improved the U-Net structure within ABINet to aggregate horizontal features and enhance spatial characterization.
Introduced a visual masking semantic awareness (VMSA) module to integrate language information for better visual prediction.

Main Results:

The proposed model effectively handles oblique deformation and character adhesion in Uyghur text.
The VMSA module aids in distinguishing between visually similar Uyghur characters by leveraging language context.
Ablation experiments validated the effectiveness of the individual components and the overall model.

Conclusions:

The enhanced scene Uyghur recognition model demonstrates superior performance compared to existing methods on a custom dataset.
The integration of visual and semantic information through the VMSA module is crucial for accurate Uyghur scene text recognition.