This was a problem I was working for a while. It is to do a proof of concept and see, with how much accuracy we can recognize a supermarket receipt. The scope is to try with 2 major supermarkets. One is Tesco and the other one is Sainsbury which are two of the leading supermarkets in the UK. The data set had over 2000 images and was a very challenging data set. Almost 60% for the receipts had below 20-30% accuracy (bad lighting conditions/angles/low resolution/crumpled) when the receipts are processed through the Tesseract OCR.

1. Okay, Now let’s start with a reasonable receipt. This receipt is readable for the human eye. Let see how the Tesseract reads this. (Hmm Not bad at-least we can get the supermarket name, less than 5% accuracy)

Continue reading “Optical Chracter Recognition on Super market bills” →

This article is about our final year project which dates back to 2015 AD, to re-identify people inside a camera network. Basically at the end of the day if you point a person in the CCTV and ask where did that person go it will generate a video of the person wandering around.

Okays not as worse as the sponge bob re-identification. But yea it works.  And yes the system runs real time too.

The following video shows the system running. That’s a very long video I would suggest to keep on reading. 😀

Continue reading “A GPU Based Real Time People Re-identification Inside a Came Network” →

Background Subtraction/Foreground detection is a very important step in detecting interesting objects in the footage. There are several methods to do this,
1. Absolute Difference Method
2. MOG (Mixture of Gaussian)
3. MOG 2
4. GMG

In this post, I will discuss the MOG technique for the background subtraction.

Continue reading “Background Substraction with MOG2” →