21,443 research outputs found
Learning-based license plate detection using global and local features
This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments. © 2006 IEEE
Boilerplate Removal using a Neural Sequence Labeling Model
The extraction of main content from web pages is an important task for
numerous applications, ranging from usability aspects, like reader views for
news articles in web browsers, to information retrieval or natural language
processing. Existing approaches are lacking as they rely on large amounts of
hand-crafted features for classification. This results in models that are
tailored to a specific distribution of web pages, e.g. from a certain time
frame, but lack in generalization power. We propose a neural sequence labeling
model that does not rely on any hand-crafted features but takes only the HTML
tags and words that appear in a web page as input. This allows us to present a
browser extension which highlights the content of arbitrary web pages directly
within the browser using our model. In addition, we create a new, more current
dataset to show that our model is able to adapt to changes in the structure of
web pages and outperform the state-of-the-art model.Comment: WWW20 Demo pape
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