Text & Sign DetectionResearch < Jerod Weinman < CompSci < Grinnell

Detection Model

Detection Model
Figure 1. A model for contextual detection. White boxes relate image features to a single unknown label y (sign or background), while green boxes associate image features such as texture gradients with pairs of neighboring labels.

Most previous work has either used (1) a sliding window approach to independently detect individual windows as text (or sign) or (2) an uninformed segmentation method followed by a classification of each region as text versus background.

Our model captures the dependency between neighboring regions and uses powerful texture features to discriminate between signs and uninteresting areas.

Example Results

Scene Image Detected Sign Detected Sign
Scene Image Detected Sign
Using a contextual model to eliminate isolated false positives and more fully cover all regions of a detected sign, we are able to robustly detect text and logos with arbitrary sizes and layouts in complex scenes.


Scene Image Detected Sign Detected Sign Scene Image
Scene Image Detected Sign Detected Sign Scene Image
Using a sliding-window classification approach (lefthand pair above) often fails to detect many sign regions, while the contextual model (righthand pair above) uses the dependencies to more fully cover signs.

Related Papers

  • Sign Detection in Natural Images with Conditional Random Fields, with A. Hanson and A. McCallum. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 549-558. Sept. 2004 [PS.gz] [PDF] [bib] [doi]
  • Preliminary work appears in the above paper. It is greatly expanded in the PhD thesis, chapter 3.
  • Publicly available data set (with ground truth) used for training and testing.