Reading Digits in Natural Images with Unsupervised Feature Learning
- Yuval Netzer, Tao Wang, Adam Coates, A. Bissacco, Bo Wu, A. Ng
- Computer Science
- 2011
A new benchmark dataset for research use is introduced containing over 600,000 labeled digits cropped from Street View images, and variants of two recently proposed unsupervised feature learning methods are employed, finding that they are convincingly superior on benchmarks.
PhotoOCR: Reading Text in Uncontrolled Conditions
- A. Bissacco, M. Cummins, Yuval Netzer, H. Neven
- Computer ScienceIEEE International Conference on Computer Vision
- 1 December 2013
This work describes Photo OCR, a system for text extraction from images that is capable of recognizing text in a variety of challenging imaging conditions where traditional OCR systems fail, notably in the presence of substantial blur, low resolution, low contrast, high image noise and other distortions.
Keyword Optimization in Search-Based Advertising Markets
- Yuval Netzer, Y. Mansour
- Economics, Computer Science
- 2011
This work abstracts an optimization problem in which the advertisers set a daily budget and select a set of keywords on which they bid, and studies the advertisers’ keyword optimizationProblem in three different settings: in an offline problem setting in which all problem parameters are known beforehand, in a stochastic model in whichthe advertiser knows only some of the parameters of the stochorian model, and in an adversarial model which makes no statistical assumptions about the generation of the query sequences.