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Journal Article (Refereed)
September 2013

Distinction Between Handwritten and Machine Printed Text Based on the Bag of Visual Words Model

Zagoris, K & Pratikakis, I & Antonacopoulos, A & Gatos, B & Papamarkos, N 2013, 'Distinction Between Handwritten and Machine Printed Text Based on the Bag of Visual Words Model', Pattern Recognition.

Abstract

In a variety of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may coexist in the same document image, raising significant issues within the recognition pipeline. It is, therefore, necessary to separate the two types of text so that it becomes feasible to apply different recognition methodologies to each modality. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words model (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a decision scheme which relies upon the combination of binary SVM classifiers. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with new datasets dedicated to the problem upon consideration.

Notes

This paper presents a real-world system that separates handwritten from printed text in scanned documents (as part of digitization workflows). This addresses a major issue in archive digitization where character recognition typically fails when both types of text are present. This paper makes two main contributions: First, this is the first paper presenting a system that can work with a large variety of document types, a critical requirement for large-scale digitization. Second, three comprehensive datasets (compiled from major European archives) are presented together with two evaluation methodologies, making results reproducible and setting a standard for future research in this area.

Authors

SEEK Members

External Authors

Ioannis Pratikakis

Konstantinos Zagoris

Nikos Papamarkos

Basilis Gatos

Publication Details

Journal Name
Pattern Recognition