Authors : Zurain Jamil Mirza; Dr. Mehwish Mirza

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/4uc3by3v

Scribd : https://tinyurl.com/ytvve58y

DOI : https://doi.org/10.38124/ijisrt/25dec005

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Abstract : This paper introduces a design and implementation of an automated system of real-time object recognition and sorting, tailored to waste sorting. The system combines a conveyor belt, color sensor, Raspberry Pi camera, and microcontroller (Arduino) to identify objects and sort them into proper categories. OpenCV computer vision algorithms can convert to grayscale, threshold, extract contours, and estimate size using perimeters. It also includes Object recognition (via ORB-based feature matching) and optical character recognition (OCR) with Tesseract to read labels or printed text on waste containers. A Python/Tkinter front-end allows real-time observability of the sorting process. Experimental analysis on a varied sample group exhibits robust performance, with perimeter-based analysis showing about 97% accuracy, feature matching at 95 percent, and OCR attaining 92 percent. These findings show that the system can effectively detect, categorize, and group waste materials with accuracy that is worthy of use in real-life situations. The next generation can involve more frame rate image capture, better lighting optimization, and hardware acceleration to increase throughput and scalability. On the whole, this research shows that a low-cost computer- vision-based architecture can be successfully used to automate waste segregation processes, providing a viable alternative to manual sorting operations.

Keywords : Automated Waste Segregation,Computer Vision,Object Detection, Feature Matching,OCR,Raspberry Pi,Conveyor System.

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