Autonomous vision-based QR code recognition

Muhammad Mu'az, Jaafar (2015) Autonomous vision-based QR code recognition. Project Report. UTeM. (Submitted)

Text AUTONOMOUS VISION-BASED QR CODE RECOGNITION 24pages.pdf Restricted to Repository staff only until 10 December 2019. Download (584kB)

Abstract

Machine vision is adaptive and dynamic systems that are able to identify an object. Nowadays, there are many processes in the industry that uses machine vision system is to act as a quality control standard of care and quality of a product. So, this project focused on the use of this system to the QR code. This system can identify the recognition QR code to extract data stored in it. Webcam camera will be used in this project to see the image QR codes. Webcam camera will detect the QR code image and the image will undergo imaging process that uses software HALCON. QR code image must be trained in the training phase to create a template object and used again in the recognition phase of object classification. The image will undergo the Reed Solomon Error to allow correct reading even if a portion of the barcode is damaged. After that, the image will undergo the binarization and gray conversion process. The image will flow to the filter process to filter the noise and edge detection process is to recognize the whole edge of image to classify the image. Then, the smoothing process is to smooth the image and the image will undergo to image recognition which is use template matching method. Template matching method is used to call the trained model in the training phase to identify the uncertainties inherent in the image. When the image beyond recognition phase, the data contained in the image will be extracted and will use the graphical user interface (GUI) to connect to the database to ensure that data stored in the image is displayed on a computer connected to the Internet.

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