International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 2
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Real-Time Retail Billing Automation with YOLO11 Product Detection and Invoice Extraction

Author(s) Ms. Florina Selvyn Correia, Rajesh Bansode
Country India
Abstract Traditional billing systems in retail stores have been found to be inaccurate and inefficient. Even with high-technology advanced automated item recognition, only a 70–78% accuracy rate can be obtained in a limited range of 12–19 product categories. Invoice processing systems can only reach a maximum of 80% accuracy and require more than four seconds per transaction. The proposed study aims to develop an integrated AI-based system using object detection with YOLO11 and document understanding with LayoutLMv3, which can perform a completely automated end-to-end billing system. The visual recognition part is carried out using YOLO11, which is capable of recognizing 30 commonly sold items in a store with 98.94% mean average precision (mAP). The model is trained using a curated dataset that has more than 100 images for each product type, thereby improving performance significantly compared to other techniques. For invoice processing, the model is trained using 7,500 synthetic invoices for 50 diverse layouts to extract billing information with 99.95% accuracy. The complete system takes approximately 1.8 seconds to process transactions when run on an NVIDIA Tesla T4 GPU.
Keywords Document Understanding; Invoice Extraction; YOLO Object Detection; LayoutLM Transformer; Automated Billing Systems; Real Time Processing; Deep Learning; Retail Au tomation
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-08
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E-ISSN 2582-2160
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