Baselines Extraction from Curved Document Images via Slope Fields Recovery (English)

In: IEEE Transactions on Pattern Analysis and Machine Intelligence   ;  42 ,  4  ;  793-808  ;  2020

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Baselines estimation is a critical preprocessing step for many tasks of document image processing and analysis. The problem is very challenging due to arbitrarily complicated page layouts and various types of image quality degradations. This paper proposes a method based on slope fields recovery for curved baseline extraction from a distorted document image captured by a hand-held camera. Our method treats the curved baselines as the solution curves of an ordinary differential equation defined on a slope field. By assuming the page shape is a smooth and developable surface, we investigate a type of intrinsic geometric constraints of baselines to estimate the latent slope field. The curved baselines are finally obtained by solving an ordinary differential equation through the Euler method. Unlike the traditional text-lines based methods, our method is free from text-lines detection and segmentation. It can exploit multiple visual cues other than horizontal text-lines available in images for baselines extraction and is quite robust to document scripts, various types of image quality degradation (e.g., image distortion, blur and non-uniform illumination), large areas of non-textual objects and complex page layouts. Extensive experiments on synthetic and real-captured document images are implemented to evaluate the performance of the proposed method.

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    Baselines Extraction from Curved Document Images via Slope Fields Recovery
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    IEEE
  • Year of publication:
    2020
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    Article (Journal)
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    Electronic Resource
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    English
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Table of contents – Volume 42, Issue 4

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The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

765
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
Kuehne, Hilde / Richard, Alexander / Gall, Juergen | 2020
780
Automated Video Face Labelling for Films and TV Material
Parkhi, Omkar M. / Rahtu, Esa / Cao, Qiong / Zisserman, Andrew | 2020
793
Baselines Extraction from Curved Document Images via Slope Fields Recovery
Meng, Gaofeng / Pan, Chunhong / Xiang, Shiming / Wu, Ying | 2020
809
Deep Self-Evolution Clustering
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824
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909
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939
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning
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956
Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding
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Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets
Clough, James R. / Balfour, Daniel R. / Cruz, Gastao / Marsden, Paul K. / Prieto, Claudia / Reader, Andrew J. / King, Andrew P. | 2020
998
Denoising Autoencoders for Overgeneralization in Neural Networks
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1005
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
Veksler, Olga | 2020
1013
Learning Raw Image Reconstruction-Aware Deep Image Compressors
Punnappurath, Abhijith / Brown, Michael S. | 2020
1020
2020 COMPSAC CFP
| 2020
C1
Table of Contents
| 2020
C2
Cover
| 2020