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Related Experiment Video

Updated: Dec 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of

Zhiwei Huang^1,2, Jinzhao Lin³, Hongzhi Yang³

¹School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)

|May 28, 2020

Summary

This study introduces a novel scene text recognition algorithm that corrects slanted text using a text position correction (TPC) module and identifies it with an encoder-decoder network (EDN). The method effectively recognizes irregular text in natural scenes, improving accuracy.

Keywords:

encoder-decoder network scene text recognition text position correction visual sensor

Related Experiment Videos

Last Updated: Dec 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Document Image Analysis

Background:

Text recognition in natural scenes is a challenging area within computer vision.
Existing methods primarily focus on horizontal text, leaving irregular and slanted text recognition largely unsolved.
Natural scene text often exhibits significant inclination and irregularity, posing difficulties for current algorithms.

Purpose of the Study:

To develop an advanced scene text recognition algorithm capable of handling irregular and slanted text.
To improve the accuracy and robustness of text recognition in complex natural scene images.
To address the limitations of previous methods that struggle with non-horizontal text.

Main Methods:

A novel algorithm integrating a text position correction (TPC) module with an encoder-decoder network (EDN) is proposed.
The TPC module preprocesses images by correcting slanted text into a horizontal orientation.
The EDN module then performs accurate text recognition on the corrected, horizontally aligned text.

Main Results:

Experimental results on standard datasets demonstrate the algorithm's effectiveness in recognizing diverse irregular text.
The proposed method achieves superior performance compared to existing approaches for scene text recognition.
Ablation studies confirm that both the TPC and EDN modules significantly enhance recognition accuracy for irregular text.

Conclusions:

The proposed TPC and EDN based algorithm offers a robust solution for scene text recognition, particularly for irregular text.
The method successfully addresses the challenge of slanted and non-standard text orientations in natural images.
This work contributes to advancing the field of document-image analysis and visual sensing applications.