Recognition of multi-oriented, multi-sized, and curved text

Yao Yi Chiang, Craig A. Knoblock

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

Text recognition is difficult from documents that contain multi-oriented, curved text lines of various character sizes. This is because layout analysis techniques, which most optical character recognition (OCR) approaches rely on, do not work well on unstructured documents with non-homogeneous text. Previous work on recognizing non-homogeneous text typically handles specific cases, such as horizontal and/or straight text lines and single-sized characters. In this paper, we present a general text recognition technique to handle non-homogeneous text by exploiting dynamic character grouping criteria based on the character sizes and maximum desired string curvature. This technique can be easily integrated with classic OCR approaches to recognize non-homogeneous text. In our experiments, we compared our approach to a commercial OCR product using a variety of raster maps that contain multi-oriented, curved and straight text labels of multi-sized characters. Our evaluation showed that our approach produced accurate text recognition results and outperformed the commercial product at both the word and character level accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
Pages1399-1403
Number of pages5
DOIs
StatePublished - 2011
Externally publishedYes
Event11th International Conference on Document Analysis and Recognition, ICDAR 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Conference

Conference11th International Conference on Document Analysis and Recognition, ICDAR 2011
Country/TerritoryChina
CityBeijing
Period9/18/119/21/11

Keywords

  • curved text
  • map processing
  • ocr
  • raster maps

Fingerprint

Dive into the research topics of 'Recognition of multi-oriented, multi-sized, and curved text'. Together they form a unique fingerprint.

Cite this