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Handwritten and Printed Text Separation in Real Document

Abdel Belaïd 1 Santosh K.C. 1 Vincent Poulain d'Andecy 2 
1 READ - Recognition of writing and analysis of documents
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : The aim of the paper is to separate handwritten and printed text from a real document embedded with noise, graphics including annotations. Relying on run-length smoothing algorithm (RLSA), the extracted pseudo-lines and pseudo-words are used as basic blocks for classification. To handle this, a multi-class support vector machine (SVM) with Gaussian kernel performs a first labelling of each pseudo-word including the study of local neighbourhood. It then propagates the context between neighbours so that we can correct possible labelling errors. Considering running time complexity issue, we propose linear complexity methods where we use k-NN with constraint. When using a kd-tree, it is almost linearly proportional to the number of pseudo-words. The performance of our system is close to 90%, even when very small learning dataset are used, where samples are basically composed of complex administrative documents.
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Contributor : Santosh K.C. Connect in order to contact the contributor
Submitted on : Tuesday, March 19, 2013 - 2:24:39 PM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM
Long-term archiving on: : Sunday, April 2, 2017 - 2:44:13 PM


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  • HAL Id : hal-00799331, version 2
  • ARXIV : 1303.4614



Abdel Belaïd, Santosh K.C., Vincent Poulain d'Andecy. Handwritten and Printed Text Separation in Real Document. The Thirteenth IAPR International Conference on Machine Vision Applications - 2013, May 2013, Kyoto, Japan. ⟨hal-00799331v2⟩



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