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Segmentation and Beautification of Handwriting using Mobile Devices
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Converting handwritten or machine printed documents into a computer readable format allows more efficient storage and processing. The recognition of machine printed text is very reliable with today's technology, but the recognition of offline handwriting still remains a problem to the research community due to the high variance in handwriting styles. Modern mobile devices are capable of performing complex tasks such as scanning invoices, reading traffic signs, and online handwriting recognition, but there are only a few applications that treat offline handwriting.

This thesis investigates the segmentation of handwritten documents into text lines and words, how the legibility of handwriting can be increased by beautification, as well as implementing it for modern mobile devices. Text line and word segmentation are crucial steps towards implementing a complete handwriting recognition system.

The results of this thesis show that text line and word segmentation along with handwriting beautification can be implemented successfully for modern mobile devices and a survey concluding that the writing on processed documents is more legible than their unprocessed counterparts. An application for the operating system iOS is developed for demonstration. 

Place, publisher, year, edition, pages
UPTEC F, ISSN 1401-5757 ; 15016
Keyword [en]
handwriting, segmentation, HWR, OCR, computer vision, opencv, iOS, mobile
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-251948OAI: oai:DiVA.org:uu-251948DiVA: diva2:808277
External cooperation
Bontouch AB
Educational program
Master Programme in Engineering Physics
Available from: 2015-05-11 Created: 2015-04-27 Last updated: 2015-05-11Bibliographically approved

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