Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Character recognition in natural images: Testing the accuracy of OCR and potential improvement by image segmentation
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In recent years, reading text from natural images has gained renewed research attention. One of the main reasons for this is the rapid growth of camera-based applications on smart phones and other portable devices. With the increasing availability of high performance, low-priced, image-capturing devices, the application of scene text recognition is rapidly expanding and becoming increasingly popular. Despite many efforts, character recognition in natural images, is still considered a challenging and unresolved problem. The difficulties stem from the fact that natural images suffer from a wide variety of obstacles such as complex backgrounds, font variation, uneven illumination, resolution problems, occlusions, perspective effects, just to mention a few. This paper aims to test the accuracy of OCR in character recognition of natural images as well as testing the possible improvement in accuracy after implementing three different segmentation methods.The results showed that the accuracy of OCR was very poor and no improvments in accuracy were found after implementing the chosen segmentation methods.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-187991OAI: oai:DiVA.org:kth-187991DiVA: diva2:932897
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2016-06-03 Created: 2016-06-02 Last updated: 2016-06-03Bibliographically approved

Open Access in DiVA

fulltext(575 kB)118 downloads
File information
File name FULLTEXT01.pdfFile size 575 kBChecksum SHA-512
9aa050595481d1cbec584821c1df4b982799af6b5b2255859d4d9466b748b20b6b1b0b407276a6d39c58636ba33dcdd33b4cf435f7d3d20a21bd1ea18d7f5489
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 118 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 635 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf