The mobile phone as a platform for assisting the independent living ...

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Fig. 13: Performing OCR on a photograph of a medication boxOptical Character Recognition is achieved with the help of Microsoft Office DocumentImaging (MODI) object model. MODI is a Microsoft Office application that supportsediting documents scanned by Microsoft Office Document Scanning. It was firstintroduced in Microsoft Office XP and is included in later Office versions includingOffice 2003 and Office 2007. One of the MODI features is an OCR engine that it allowsusers to produce editable text from scanned documents, interpreting pixel-based imagesto layout and text elements.7.4 Optimizing the resultsWe tested our application with a number of photographs captured by a ΗTC P3650smart phone. The results derived from our experiments were discouraging, so we didseveral efforts to optimize the performance of our application.A well-known method that can be used to increase the image quality and thus, the OCRresults, is performing image processing techniques to the photographs. We used AdobePhotoshop CS2 to apply image processing functions to photographs of medication boxesin order to achieve a better performance of the OCR. The image processing techniquesthat we performed are:• Contrast adjustment• Level adjustment• Grayscale• Sharpness filter• Threshold adjustment43
Fig. 14: Use of image processing techniques to optimize the OCR resultsApart from performing image processing techniques to the photographs of medicationboxes, we decided to optimize the results by taking advantage of an a priori knownmedication list. For this reason we added to our project an XML file (Fig. 14) containingthe names of the medications used in our experiments.CECLORTENORMIN...BactrimelULTRA LEVUREFig. 15: A medication list written in XMLWith this modification, we perform OCR on the photographs of medication boxes andin the text produced we search for the exact names using the medication list. If none ofthe medication names is found, we calculate the similarity between the OCR text andeach one of the medication names and return the best result. The similarity level between44
Page 1: ATHENS UNIVERSITY OF ECONOMICS AND
Page 5 and 6: AbstractMobile phones or Personal D
Page 7 and 8: Chapter 7: Implementation of an app
Page 9 and 10: List of TablesTable 1: Advantages a
Page 11 and 12: suffering from memory and orientati
Page 13 and 14: Chapter 2Smart Homes2.1 Smart Homes
Page 15 and 16: • Automate specific tasks that a
Page 17 and 18: Fig. 1: A smart home solution 1It i
Page 19 and 20: lighting strips and passive infra-r
Page 21 and 22: The Amigo project [10] is a joint e
Page 23 and 24: However, inside a building, that un
Page 25 and 26: with co-ordinates in the form of la
Page 27 and 28: At last, the utilized technology mu
Page 29 and 30: In this work we concentrate on achi
Page 31 and 32: In order to estimate the user locat
Page 33 and 34: 4.2.1 Cell-based methodsCell-based
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Page 37 and 38: thus increasing the storage and mem
Page 39 and 40: egions. Examining images based on t
Page 41 and 42: Chapter 6Implementation of an indoo
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Page 45 and 46: Fig. 10: The database table that st
Page 47 and 48: Fig.12: The matching stageThe query
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Page 57 and 58: Chapter 8Conclusion8.1 ConclusionMo
Page 59 and 60: [13] Gilles Virone, Norbert Noury,
Page 61 and 62: [40] Andrew M. Ladd, Kostas E. Bekr
Page 63: [65] X. Chen and A.L. Yuille, “A

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