A stroke regeneration method for cleaning rule-lines in handwritten document images

Authors:
Huaigu Cao;Rohit Prasad;Prem Natarajan
Affiliations:
BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA
Venue:
Proceedings of the International Workshop on Multilingual OCR
Year:
2009

Citing 6
Cited 2

Mending broken handwriting with a macrostructure analysis method to improve recognition

Pattern Recognition Letters
Line Removal and Restoration of Handwritten Characters on the Form Documents

ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Separation of Overlapping Text from Graphics

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Dynamical Morphological Processing: A Fast Method for Base Line Extraction

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Line Removal and Restoration of Handwritten Strokes

ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 03
Multi-lingual offline handwriting recognition using hidden Markov models: a script-independent approach

SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition

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
Model-based ruling line detection in noisy handwritten documents

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a rule-line removal algorithm for handwritten document images in this paper. Compared to the existing approaches, our algorithm obtains more scalability to higher-resolution images and thicker rule-lines. Derived from the simple gap-filling methods using line-drawing algorithms, we present a novel approach to regenerating the missing portions of text strokes. Using this approach, the deformed text can be restored to its original shape. We also explore the noise filtering method for binarized document images, in particular by choosing the morphological operator in accordance with the noise power of the input image. Our approach has proven to be effective by experiments on both real and synthetic handwritten document images.