A Model-Based Ruling Line Detection Algorithm for Noisy Handwritten Documents

Authors:
Jin Chen;Daniel Lopresti
Affiliations:
-;-
Venue:
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Year:
2011

Citing 0
Cited 3

A real-world noisy unstructured handwritten notebook corpus for document image analysis research

Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Ruling-based table analysis for noisy handwritten documents

Proceedings of the 4th International Workshop on Multilingual OCR
Model-based ruling line detection in noisy handwritten documents

Pattern Recognition Letters

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Abstract

Ruling lines are commonly used to help people write neatly on paper. In document image analysis, however, they create challenges for handwriting recognition and writer identification. In this paper, we model ruling line detection as a multi-line linear regression problem and then derive a globally optimal solution giving the Least Square Error. We demonstrate the efficacy of the technique on both synthetic and real datasets. A comparative study shows that our algorithm outperforms a previously published method on the public Germana dataset.