Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Binarising camera images for OCR
57
Zitationen
2
Autoren
2002
Jahr
Abstract
We describe a 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.
Ähnliche Arbeiten
Deep Residual Learning for Image Recognition
2016 · 219.210 Zit.
ImageNet: A large-scale hierarchical image database
2009 · 61.210 Zit.
Distinctive Image Features from Scale-Invariant Keypoints
2004 · 55.008 Zit.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
2016 · 53.448 Zit.
Going deeper with convolutions
2015 · 46.570 Zit.