International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024
p-ISSN: 2395-0072
www.irjet.net
Smart-Bill Automation System Prof. Nilaja Deshmukh1, Sejal Kathane2, Gayatri Suryawanshi3, Shreya Mahatme4,Parikshit Gaikwad5 1Professor, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 2Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
3Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
4Student , Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
5Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.INTRODUCTION Abstract - The ever-evolving retail sector demands
innovative solutions to streamline and enhance the shopping experience for both consumers and retailers. This paper introduces an AI-based auto billing system meticulously designed to address the inherent complexities of the traditional retail checkout process, especially concerning items like fruits and vegetables that defy conventional tagging mechanisms such as RFID tags and bar-codes. Recognizing the distinct challenges posed by perishable goods, the proposed system integrates advanced technologies, notably computer vision and deep learning techniques, to automate the weighing and billing processes seamlessly. Central to this innovative solution is the utilization of Raspberry Pi 3, a microprocessor equipped with a specialized camera module and a load cell. The system's operational framework encompasses capturing high-resolution images of individual fruits and vegetables, leveraging deep learning techniques such as ImageNet built upon Convolutional Neural Network (CNN) architectures. Further enhancing the classification efficacy, machine learning techniques, including K means clustering, are employed to categorize products into their respective groups, ensuring precise billing calculations. Incorporating a multifaceted approach, the camera module facilitates realtime image capturing of items positioned on a designated tray, strategically integrated with a load cell for accurate weight measurements. Concurrently, pre-defined pricing metrics for various items per kilogram are inputted into the Raspberry Pi microprocessor. Leveraging the computational prowess of the Python programming language, the system orchestrates intricate algorithms to compute the cumulative cost of selected items, subsequently displaying the total amount on an integrated monitor. Emphasizing paramount aspects such as efficiency, accuracy, and safety, this AI-based autobilling system transcends conventional retail paradigms, offering a transformative solution that harmonizes technological innovation with pragmatic retail requirements. By seamlessly amalgamating cutting-edge technologies, this pioneering system not only elevates the retail experience but also underscores the potential of AI-driven solutions in redefining modern retail landscapes.
The rapid trajectory of technological advancements in recent decades has fundamentally reshaped the fabric of our daily lives, ushering in an era characterized by unparalleled convenience, efficiency, and innovation. The contemporary technological landscape is punctuated by an eclectic array of transformative technologies, encompassing artificial intelligence (AI), blockchain, cloud computing, the Internet of Things (IoT), data mining, augmented reality (AR), and virtual reality (VR), among others. Each of these innovations, in its unique capacity, has catalyzed paradigm shifts across diverse sectors, redefining operational frameworks, enhancing user experiences, and fostering unprecedented possibilities. Central to this mosaic of technological innovations is the realm of artificial intelligence, an expansive domain that encapsulates machine learning and deep learning paradigms. Within this ambit, deep learning, a subset of machine learning, has emerged as a cornerstone, revolutionizing intricate processes and catalyzing advancements previously deemed implausible. The intrinsic architecture of deep neural networks, reminiscent of the intricate operations of the human brain, facilitates unparalleled capabilities in pattern recognition, decisionmaking, speech recognition, and myriad other applications. The omnipresence of neural networks is palpable, underpinning transformative applications ranging from ubiquitous virtual assistants like Google's Assistant and Apple's Siri to intricate financial services, encompassing fraud detection, risk assessment, and market research endeavors. In alignment with this transformative trajectory, the crux of this project endeavors to harness the latent potential of deep learning algorithms, specifically Convolutional Neural Networks (CNN), and machine learning techniques, exemplified by K-means clustering. The overarching objective converges on designing an innovative system poised to revolutionize the retail landscape, particularly the billing process. By adeptly leveraging deep learning algorithms, the system aspires to seamlessly identify an expansive array of edible fruits and vegetables, transcending manual interventions and encapsulating efficiency. Furthermore, augmenting its capabilities, the integration of load cell technology facilitates automated weight measurements, culminating in precise cost estimations and streamlined billing processes. In essence, this project epitomizes the symbiotic amalgamation of
Key Words: Billing System, Checkout Mechanism, Retail Technology, Artificial Intelligence(AI) in Retail, Computer Vision, Deep Learning in Retail, Convolutional Neural Networks (CNN),Weight Sensing Technology, Raspberry Pi in Retail, Automated Checkout.
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