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

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.

Citation

Zagoris, K., Pratikakis, I., Antonacopoulos, A., Gatos, B., & Papamarkos, N. (2014). Distinction between handwritten and machine-printed text based on the bag of visual words model. Pattern recognition, 47(3), 1051-1062. https://doi.org/10.1016/j.patcog.2013.09.005

• Journal Article Type: Article
• Publication Date: Jan 1, 2014
• Deposit Date: May 7, 2014
• Journal: Pattern Recognition
• Print ISSN: 0031-3203
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 47
• Issue: 3
• Pages: 1051-1062
• DOI: https://doi.org/10.1016/j.patcog.2013.09.005
• Keywords: Bag of visual words; Local features; Support vector machines; Page layout
• Publisher URL: http://dx.doi.org/10.1016/j.patcog.2013.09.005
• Related Public URLs: http://www.elsevier.com/wps/product/cws_home/328/description