Binarization and multithresholding of document images using connectivity
CVGIP: Graphical Models and Image Processing
Machine vision
Document Image Binarization Based on Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
The Document Spectrum for Page Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Perspective rectification of camera-based document images using local linear structure
Proceedings of the 2008 ACM symposium on Applied computing
Image-Based Techniques for Shredded Document Reconstruction
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Word-Wise Thai and Roman Script Identification
ACM Transactions on Asian Language Information Processing (TALIP)
Fuzzy Sets and Systems
Reconstruction of shredded document based on image feature matching
Expert Systems with Applications: An International Journal
Language identification in degraded and distorted document images
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
The restoration of camera documents through image segmentation
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Enhancing document images acquired using portable digital cameras
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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In this paper, we deal with the problem of document image rectification from image captured by digital cameras. The improvement on the resolution of digital camera sensors has brought more and more applications for non-contact text capture. Unfortunately, perspective distortion in the resulting image makes it hard to properly identify the contents of the captured text using traditional optical character recognition (OCR) systems. We propose in this work a new technique, which is capable of removing perspective distortion and recovering the fronto-parallel view of text with a single image. Different from reported approaches in the literature, the image rectification is carried out using character stroke boundaries and tip points (SBTP), which are extracted from character strokes based on multiple fuzzy sets and morphological operators. The algorithm needs neither high-contrast document boundary (HDB) nor paragraph formatting (PF) information. Experimental results show that our rectification process is fast and robust.