Fast prototyping of image processing application on android platform focused on PDF417 decoder

Dedicated Hardware for two dimensional (2D) barcodes is used in numerous applications depending on the levels of preserving data in complex format. These modules are complex in their design and also take considerable amount of time for their development, testing and verification both at software and hardware levels. On the other hand, modern android mobile/tablets are ready to use hardware platform that fulfills all the essential hardware requirements to implement the extraction of barcodes. Additionally, they got the capability to run the complex image processing algorithms on application level, which increases the prototyping speed considerably for 2D barcode implementation. In order to reduce the algorithm complexity further, different techniques have been devised on hardware level such as light conditioning and orientation, placement of barcode and distance from the camera. The system has been effectively deployed for the decoding of PDF 417 codes on student university cards at the university. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:596–602, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21630

[1]  Luís Nogueira,et al.  Evaluating Android OS for Embedded Real-Time Systems , 2010 .

[2]  László G. Nyúl,et al.  A Novel Method for Accurate and Efficient Barcode Detection with Morphological Operations , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[3]  Tat-Jun Chin,et al.  Exact integral images at generic angles for 2D barcode detection , 2008, 2008 19th International Conference on Pattern Recognition.

[4]  Alexis Berelowitch Les totalitarismes de Vassili Grossman , 2011 .

[5]  Xudong Jiang,et al.  Dynamic window construction for the binarization of barcode images captured by mobile phones , 2010, 2010 IEEE International Conference on Image Processing.

[6]  Sabine Süsstrunk,et al.  Cell phones as imaging sensors , 2010, Defense + Commercial Sensing.

[7]  Bo Yang,et al.  Bar Code Recognition in Complex Scenes by Camera Phones , 2008, 2008 Fourth International Conference on Natural Computation.

[8]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[9]  Hiroshi Hanaizumi,et al.  Barcode readers using the camera device in mobile phones , 2004, 2004 International Conference on Cyberworlds.

[10]  Xudong Jiang,et al.  Binarization of Low-Quality Barcode Images Captured by Mobile Phones Using Local Window of Adaptive Location and Size , 2012, IEEE Transactions on Image Processing.

[11]  Ki-Cheol Son,et al.  The method of android application speed up by using NDK , 2011, 2011 3rd International Conference on Awareness Science and Technology (iCAST).

[12]  Wei Xu,et al.  2D Barcode localization and motion deblurring using a flutter shutter camera , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[13]  Ying-Hong Liang,et al.  Real Time Recognition of 2D Bar Codes in Complex Image Conditions , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[14]  Ying-Hong Liang,et al.  A Skew Distortion Correction Method for 2D Bar Code Images Based on Vanishing Points , 2007, 2007 International Conference on Machine Learning and Cybernetics.