Textline information extraction from grayscale camera-captured document images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A new algorithm for segmenting warped text-lines in document images
Proceedings of the 2011 ACM Symposium on Applied Computing
A new method for text-line segmentation for warped documents
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Border noise removal of camera-captured document images using page frame detection
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
The optical character recognition of Urdu-like cursive scripts
Pattern Recognition
Hi-index | 0.00 |
As compared to scanners, cameras offer fast, flexible and non-contact document imaging, but with distortions like uneven shading and warped shape. Therefore, camera-captured document images need preprocessing steps like binarization and textline detection for dewarping so that traditional document image processing steps can be applied on them. Previous approaches of binarization and curled textline detection are sensitive to distortions and loose some crucial image information during each step, which badly affects dewarping and further processing. Here we introduce a novel algorithm for curled textline region detection directly from a grayscale camera-captured document image, in which matched filter bank approach is used for enhancing textline structure and then ridges detection is applied for finding central line of curled textlines. The resulting ridges can be potentially used for binarization, dewarping or designing new techniques for camera-captured document image processing. Our approach is robust against bad shading and high degrees of curl. We have achieved around 91% detection accuracy on the dataset of CBDAR 2007 document image dewarping contest.