Scanning Severe Motion Blur Level in Barcode Images Using Image Blur Estimation Scheme

The paper presents a fresh linear barcode scanning system based on hybrid template matching scheme. In current area, charge coupled device based scanning skills are not talented of handling motion blur image and trust severely on camera systems for capturing good quality, well focused barcode images which are due to the lack of selfcontrolled and proficient mechanisms. The proposed system is capable of understanding barcodes from lowresolution images containing severe motion blur and works totally in the dimensional domain. The proposed system also uses image blur estimation scheme for retrieving the severe motion blur in barcode images. We first estimate a lowdimensional approximation to the PSF by using some parts of clean barcode that are known by construction. This lowdim representation only involves a few parameters, which can be iteratively computed via the Levenberg-Marquardt (LM) algorithm. Polynomial interpolations are obtained in the rest of the PSF. A focused graphical model is designed to characterize the relationship between the blurred barcode waveform and its corresponding symbol value at any specific blur level. A hybrid programming-based inference algorithm is designed to regain the optimal state series, enabling synchronized decoding on mobile devices of limited processing power.