Paper Reading AI Learner

Geometric Rectification of Creased Document Images based on Isometric Mapping

2022-12-16 09:33:31
Dong Luo, Pengbo Bo

Abstract

Geometric rectification of images of distorted documents finds wide applications in document digitization and Optical Character Recognition (OCR). Although smoothly curved deformations have been widely investigated by many works, the most challenging distortions, e.g. complex creases and large foldings, have not been studied in particular. The performance of existing approaches, when applied to largely creased or folded documents, is far from satisfying, leaving substantial room for improvement. To tackle this task, knowledge about document rectification should be incorporated into the computation, among which the developability of 3D document models and particular textural features in the images, such as straight lines, are the most essential ones. For this purpose, we propose a general framework of document image rectification in which a computational isometric mapping model is utilized for expressing a 3D document model and its flattening in the plane. Based on this framework, both model developability and textural features are considered in the computation. The experiments and comparisons to the state-of-the-art approaches demonstrated the effectiveness and outstanding performance of the proposed method. Our method is also flexible in that the rectification results can be enhanced by any other methods that extract high-quality feature lines in the images.

Abstract (translated)

URL

https://arxiv.org/abs/2212.08365

PDF

https://arxiv.org/pdf/2212.08365.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot