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Multi-oriented Text Line Extraction from Handwritten Arabic Documents

Authors: Ouwayed, Nazih; Belaïd, Abdel;

Multi-oriented Text Line Extraction from Handwritten Arabic Documents

Abstract

In this paper, we present a novel approach for the multi-oriented text line extraction from handwritten Arabic documents. After image pre-processing, the local orientations are determined in small windows obtained by image paving. The orientation of the text within each window is estimated using the projection profile technique considering several projection angles. Then, the windows which close angles are gathered into largest zones. We use the Wigner-Ville Distribution (WVD) to estimate the global orientation of each zone. The WVD is more precise than the classical projection profile technique. Afterwards, the text lines are extracted in each zone basing on the follow-up of the baselines and the proximity of connected components. The experimental results prove the efficiency of the proposed scheme. It has been evaluated on 50 documents reaching an accuracy of about 97.6%.

Keywords

[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Average
Top 10%
Top 10%
Green