MLR-NET: An Arbitrary Skew Angle Detection Algorithm for Complex Layout Document Images

To avoid applying intelligent document processing techniques that are sensitive to the image with skew angle. Scanned and processed digitized document images with complex layouts (CLDImge) must be transformed to obtain a rectified image by calculating the skew angle. Traditional machine learning methods based on image characterization can only detect skew angles between plus and minus 45∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}. Meanwhile, deep learning-based classification models can only detect the discretized skew angle of a set scale, which constrains the accuracy of angle detection. Therefore, to address the limitations of detection range and granularity, we propose a multivariate linear regression network (MLR-NET) for the detection of arbitrary skew angles in CLDImge, which realizes high-precision computation of arbitrary skew angles from −180∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document} to 180∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}. MLR-NET improves the linear representation between skew angles and features by fitting multiple mapping values generated by a set continuous period mapping function instead of directly fitting the actual skew angle in the multiple linear regression layer. Further, considering that the mapping value regression prediction approach has the problem of uneven gradient conduction or even local extreme points during model training, We propose an optimization method based on a rotating coordinate system. The average error Of MLR-NET is only 0.0515 on the constructed dataset CLDIMGE-DATA, and the model effectiveness is verified on the public dataset Tobacco600.