Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Fast Lexicon-Based Word Recognition in Noisy Index Card Images
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Text Region Extraction and Text Segmentation on Cameracaptured Document Style Images
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Adaptive degraded document image binarization
Pattern Recognition
A Multi-Lingual Translation System For Real-World Images
Journal of Integrated Design & Process Science
Extracting text information for content-based video retrieval
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Correcting bound document images based on automatic and robust curved text lines estimation
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Extraction of handwritten text from carbon copy medical form images
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Efficient illumination compensation techniques for text images
Digital Signal Processing
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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Abstract: In this paper we describe a new binarisation method designed specifically for OCR of low quality camera images: Background Surface Thresholding or BST. This method is robust to lighting variations and produces images with very little noise and consistent stroke width. BST computes a "surface" of background intensities at every point in the image and performs adaptive thresholding based on this result. The surface is estimated by identifying regions of low-resolution text and interpolating neighbouring background intensities into these regions. The final threshold is a combination of this surface and a global offset. According to our evaluation BST produces considerably fewer OCR errors than Niblack's local average method while also being more runtime efficient.