First Thesis Presentation
•
1 like•1,926 views
More information about our thesis can be found on our blog: http://augmentjapan.wordpress.com/ .
![[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Recommended
More Related Content
Viewers also liked
Viewers also liked (20)
Similar to First Thesis Presentation
Similar to First Thesis Presentation (20)
Recently uploaded
Recently uploaded (20)
First Thesis Presentation
- 1. Matthias Vandenbussche Annelies Van der Borght Promotor: Erik Duval Supervisor: Sten Govaerts Stand-in Supervisor: Gonzalo Parra http://augmentjapan.wordpress.com/
- 8. 225
- 23. NHOCR Tesseract WiseTREND Aspire ABBYY Finereader
- 25. Google Translate Bing Translator SYSTRAN InterTran WordLingo myGengo OneHourTranslation Apertium F-measure BLEU NIST [0;1] [0;1] ? [0;11]
- 30. “ We are very sorry for the inconvenience” “ Sorry, my wife might read over the evil described in 菩”
- 39. Camera Augment video Locating text Tracking Sensor HTML5 iPhone Android phone PhoneGap iPhone Android phone Native app iPhone Android phone
Editor's Notes
- We found a good number of papers regarding finding text in images. A common problem among these papers, however, is the general lack of details in regards to their methods and/or uses parameters and thresholds. Other methods use Simple Vector Machines and other Machine Learning techniques, for which we do not have the required data to train them, nor the knowledge on how to use them properly. Our last hope lies with a method presented as the Stroke Width Transform. We found a C++ implementation of a variation of this method, and are currently testing to see if we can use it.
- We found a good number of papers regarding finding text in images. A common problem among these papers, however, is the general lack of details in regards to their methods and/or uses parameters and thresholds. Other methods use Simple Vector Machines and other Machine Learning techniques, for which we do not have the required data to train them, nor the knowledge on how to use them properly. Our last hope lies with a method presented as the Stroke Width Transform. We found a C++ implementation of a variation of this method, and are currently testing to see if we can use it.
- We found a good number of papers regarding finding text in images. A common problem among these papers, however, is the general lack of details in regards to their methods and/or uses parameters and thresholds. Other methods use Simple Vector Machines and other Machine Learning techniques, for which we do not have the required data to train them, nor the knowledge on how to use them properly. Our last hope lies with a method presented as the Stroke Width Transform. We found a C++ implementation of a variation of this method, and are currently testing to see if we can use it.
- We found a good number of papers regarding finding text in images. A common problem among these papers, however, is the general lack of details in regards to their methods and/or uses parameters and thresholds. Other methods use Simple Vector Machines and other Machine Learning techniques, for which we do not have the required data to train them, nor the knowledge on how to use them properly. Our last hope lies with a method presented as the Stroke Width Transform. We found a C++ implementation of a variation of this method, and are currently testing to see if we can use it.
- Masao Uchiyama Hitoshi Isahara