A multiple classifier approach for the recognition of screen-rendered text

Authors:
Steffen Wachenfeld;Stefan Fleischer;Xiaoyi Jiang
Affiliations:
Department of Computer Science, University of Münster, Münster, Germany;Department of Computer Science, University of Münster, Münster, Germany;Department of Computer Science, University of Münster, Münster, Germany
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

Citing 5
Cited 1

A Survey of Methods and Strategies in Character Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Twenty Years of Document Image Analysis in PAMI

IEEE Transactions on Pattern Analysis and Machine Intelligence
Effects of Classifier Structures and Training Regimes on Integrated Segmentation and Recognition of Handwritten Numeral Strings

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Screen-Rendered Text

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Annotated Databases for the Recognition of Screen-Rendered Text

ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01

Associating the visual representation of user interfaces with their internal structures and metadata

Proceedings of the 24th annual ACM symposium on User interface software and technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

The lower the resolution of a given text is, the more difficult it becomes to segment and to recognize it. The resolution of screen-rendered text can be very low. With a typical x-height of 4 to 7 pixels it is much lower as in other low resolution OCR situations. Modern OCR approaches for such very low resolution text use a classification-based segmentation where the underlying classifier plays an important role. This paper presents a multiple classifier system for the classification of single characters. This system is used as a subsystem for the classification-based segmentation within a system to read screen-rendered text. The paper shows that the presented multiple classifier system outperforms the best former single classifier system on single characters by far and it shows the impact of using the multiple classifier system on the word reading performance.