舰船电子工程2025，Vol.45Issue(12)：41-45,5.DOI:10.3969/j.issn.1672-9730.2025.12.009
基于偏移场学习的扭曲文档矫正
Distortion Document Rectification Based on Offset Field Learning
董前前 1陈亮 1王鑫鑫1
作者信息
- 1. 西安工程大学计算机科学学院 西安 710048
- 折叠
摘要
Abstract
The rectification of distorted document images is an essential approach to enhance text recognition and information extraction accuracy,thereby promoting the intelligentization of documents.Existing methods for correcting distorted documents pri-marily focus on tightly cropped document images and are ineffective in addressing documents with significant environmental bound-aries.To address this issue,this paper proposes a distortion document correction method that involves detection before correction.Initially,improvements to the Mask R-CNN are introduced to enhance the accuracy of detecting blurry document images.This in-cludes the incorporation of a higher-resolution ROI Align layer and decoder layer,along with optimizations to the Mask module.Subsequently,precise correction of distorted documents is achieved by introducing offset field learning.Experimental results demon-strate notable performance on the Doc3DShade dataset compared to other state-of-the-art algorithms,with a 1.6％improvement in SSIM metric,a 0.5％decrease in LD metric,and a 5.42％reduction in character error rate.These findings substantiate its excep-tional performance and provide an efficient and accurate document correction solution for document capture on mobile devices.关键词
偏移场/图像矫正/Mask R-CNN/移动设备Key words
offset field/image rectification/Mask R-CNN/mobile devices分类
信息技术与安全科学引用本文复制引用
董前前,陈亮,王鑫鑫..基于偏移场学习的扭曲文档矫正[J].舰船电子工程,2025,45(12):41-45,5.
