Automated entry system for printed documents
Pattern Recognition
Algorithm for text page up/down orientation determination
Pattern Recognition Letters
Implementation techniques for geometric branch-and-bound matching methods
Computer Vision and Image Understanding
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A fast orientation and skew detection algorithm for monochromatic document images
Proceedings of the 2005 ACM symposium on Document engineering
Automatic document orientation detection and categorization through document vectorization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
An Overview of the Tesseract OCR Engine
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Fast and Accurate Detection of Document Skew and Orientation
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Google Book Search: Document Understanding on a Massive Scale
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document cleanup using page frame detection
International Journal on Document Analysis and Recognition
Estimation of proper parameter values for document binarization
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
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In document image recognition, orientation detection of the scanned page is necessary for the following procedures to work correctly as they assume that the text is well oriented. Several methods have been proposed, but most of them rely on heuristics of the script such as the graphical asymmetry between ascenders and descenders for Roman script. The literature shows that as soon as this assumption is not fulfilled, e.g. plain capital text, noisy or degraded characters, etc. they fail. For a large-scale digitalization process, a low error and rejection rate are expected in order to reduce the amount of human intervention. We propose a Recognition Driven Page Orientation Detection (RD-POD) which does not depend on external criteria or assumption on the shape of the script. It uses the OCR engine for estimating the right orientation with a few lines of the document image. The RD-POD is highly robust and accurate, and is able to detect multiple orientations. Experimental evaluation shows that our method outperforms the current state-of-the-art on UW-1 dataset with an accuracy of 99.7%. Further tests on other three large and public datasets (MARG, ICDAR07, Google 1000 books) show accuracies of above 99% on each of them.