ML-Powered POS Recognition System

@AwaisChaudhry

As the lead developer at ZAWN Tech, I bring unrivaled expertise in the realm of AI and machine learning. My skills in image processing and recognition have been deeply honed over the past decade, ideally positioning me to deliver a high-performing ML-powered POS recognition system for your needs. The extensive experience I've had working with OCR systems, particularly those revolving around identifying objects in images, is directly relevant to your project. Moreover, my team and I keenly incorporate emerging technologies — an aspect that will play a pivotal role in making your project future-proof. My strong suit lies in delivering solutions that are both robust and scalable,and my adeptness at Python and React.js ensures great ease of system development and integration. I am confident that my tried-and-true approach to incorporating state-of-the-art ML algorithms with delicately detailed computer vision techniques will enable us to construct a solution for you that is not only comprehensive in detecting product names and categories as desired but also proficient in gauging prices, stock relevance, and shelf quality by analyzing the faces of each product available on the shelf.

@MashoodurRehman1

I am a seasoned ML/AI expert with significant experience in computer vision and image recognition tasks, ideal for developing the "ML-Powered POS Recognition System" you require. My strong background in Python programming and extensive work in retail and shelf analysis equips me to effectively address your needs for identifying product attributes such as names, categories, prices, shelf exposure, and quality based on height. My portfolio includes several successful projects where I've integrated ML models to enhance image processing systems, using state-of-the-art technologies like TensorFlow and OpenCV. I have developed sophisticated solutions for retail environments, focusing on accurate detection and categorization of goods on shelves. My proficiency in image recognition, coupled with an understanding of retail dynamics, ensures a reliable and efficient system ready for production-level deployment. I am keen to learn more about specific challenges you aim to address with this solution. Could you provide insights on any data sources you have in mind? Looking forward to discussing how I can contribute further.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Machine Learning System for Recognizing Packaged Goods ❇️ Hi My Friend, I hope you are doing well. I reviewed your project requirements and see you are looking for a machine learning system to recognize packaged goods in point of sales images. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in machine learning and image recognition. I will use efficient methods to identify product names, categories, prices, shelf availability, and quality of exposition. ➡️ Why Me? I can easily create your machine learning system as I have 5 years of experience in computer vision and image recognition. My expertise includes Python programming, data analysis, and retail analysis. Additionally, I have a strong grip on deep learning frameworks, ensuring a robust approach to your project. ➡️ Let's have a quick chat to discuss your project in detail. I would love to show you samples of my previous work. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Machine Learning ✅ Computer Vision ✅ Image Recognition ✅ Python Programming ✅ Data Analysis ✅ Retail Analysis ✅ Deep Learning ✅ TensorFlow ✅ OpenCV ✅ Model Training ✅ Data Preprocessing ✅ Performance Optimization Waiting for your response! Best Regards, Zohaib

@irsolutions

With over 10 years of experience in web and mobile development, specializing in AI/ML projects, I understand the need for a precise ML-Powered POS Recognition System that can accurately identify product details in point of sales images. Your project requires expertise in computer vision, ML, image recognition tasks, and Python programming skills, all of which align perfectly with my skill set. In my previous projects, I have successfully developed AI solutions for various industries, including retail and eCommerce, where accuracy and efficiency are paramount. My experience in building ML models for image recognition tasks, coupled with my strong programming skills in Python, will ensure the system's seamless integration and functionality. I am confident that my proven track record in AI/ML development, along with my ability to deliver tailored and efficient solutions, makes me the ideal candidate for your ML-Powered POS Recognition System. Let's discuss further how I can bring value to your project.

@leadconcept

Hello, I understand you need a machine learning system capable of analyzing point of sale images to automatically identify packaged products, classify them, read prices, count facings, and assess shelf exposition quality. The system should handle variations in lighting, angles, and occlusions while producing structured outputs for each image. A few questions to clarify, 1. Do you already have labeled datasets for training, or should dataset collection and annotation be part of the project? 2. Should the system operate in real time (e.g., on mobile or embedded devices) or batch processing on a server is acceptable? 3. Are there specific output formats or integration endpoints required for downstream systems? Our team has experience building computer vision pipelines using Python, TensorFlow, and PyTorch, including product recognition, object counting, and quality assessment in retail environments. We focus on high accuracy, robustness to real world conditions, and producing structured outputs that are easy to integrate with reporting or analytics systems. We can deliver a full pipeline with training, inference, and evaluation, and provide documentation for integration and future retraining. The current bid amount is a placeholder to submit the proposal. Regards Yasir LEADconcept PS: I can share examples of retail image recognition and shelf monitoring ML systems we have built that include object detection, counting, and category classification.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@corpmember29

Olá!! Li atentamente seus requisitos e compreendi perfeitamente o escopo do projeto, e começarei a trabalhar em etapas. Desenvolverei um sistema de aprendizado de máquina e visão computacional para analisar imagens de pontos de venda (PDV), identificando nomes de produtos, categorias, preços, disposição nas prateleiras e qualidade da exposição. O sistema será construído em Python, utilizando modelos de reconhecimento de imagem de última geração e técnicas de análise de prateleiras de varejo, com arquitetura escalável e modular para futuras melhorias. Começarei com a preparação do conjunto de dados e a prototipagem do modelo, seguida pelo treinamento, validação e implantação de um pipeline de reconhecimento preciso. >>>> Suporte multilíngue (inglês e português do Brasil) <<<< Oferecerei 2 anos de suporte contínuo gratuito e o código-fonte completo. Trabalharemos com metodologia ágil e prestarei assistência desde o início até a publicação nas lojas. Com mais de 10 anos de experiência em aprendizado de máquina, visão computacional e desenvolvimento de sistemas de nível de produção, posso entregar uma solução robusta e confiável com eficiência. Aguardo ansiosamente sua resposta positiva. Obrigado.

@Herculesz

Hi I can build a computer vision system that accurately detects packaged goods in point-of-sale images and extracts product name, category, shelf facings, and display quality. The main technical challenge is distinguishing visually similar products while handling occlusions and varying lighting conditions, and I solve this using a combination of deep learning object detection and fine-grained image classification models. I have experience with retail shelf analytics using CNN-based detectors such as YOLO and transformer-based vision models for robust recognition. Price extraction can be handled through shelf-tag detection combined with OCR and validation logic. Shelf facings and exposure quality will be computed through spatial analysis of bounding boxes and vertical positioning. The system will be designed for scalable inference and easy retraining as product assortments change. All components will be implemented in Python with clean pipelines for data ingestion, training, and evaluation. Thanks, Hercules

@hireageek

Hi there, I’m excited about the opportunity to develop a Machine Learning-powered Point of Sale recognition system. With extensive expertise in computer vision and deep learning, I understand the complexities involved in recognizing packaged goods from images. I have successfully completed similar projects where I implemented image recognition algorithms that efficiently identify product attributes including names, categories, and even pricing. Additionally, I have a solid background in retail analytics that will be invaluable for evaluating shelf exposure. To ensure a seamless implementation, I would leverage Python, utilizing frameworks such as YOLO for precise object detection. My strong programming skills and attention to detail would ensure that every aspect, from recognizing product faces to analyzing quality of exposition, is handled with expertise. Let's discuss this further! What specific metrics are most critical to you for the shelf analysis? What specific metrics are most critical to you for the shelf analysis? Thanks,

@mahmoudralizadeh

I can build an end‑to‑end computer‑vision system for POS shelf recognition, tailored to retail environments. The pipeline would detect and identify products using a modern object‑detection model (e.g., YOLO), then map detections to product metadata (name, category, price). I’ll compute number of facings per SKU directly from bounding boxes and estimate exposition quality using vertical shelf position and relative height rules. I have hands‑on experience in image recognition, object detection, and retail‑style visual analytics, including preprocessing noisy in‑store photos and handling overlapping products. The solution will be modular, well‑documented, and ready for integration (Python backend; API‑friendly). Quick question: do you already have a labeled product image dataset and price catalog, or should I include dataset structuring and annotation guidelines? I can start immediately after reviewing the briefing document.

@ShaikhAneesa

Hello, I have carefully reviewed the project requirements and fully understand the objective of developing a machine learning system capable of recognizing packaged goods in point-of-sale images. With extensive experience in computer vision and deep learning, I specialize in building robust image recognition pipelines that can identify product names, categories, prices, and shelf availability with high accuracy. My approach will start by collecting and preprocessing a representative dataset of shelf images, followed by training a YOLO or EfficientDet-based model for object detection to recognize individual products. I will integrate additional algorithms to count product faces and evaluate exposition quality using height and placement metrics. Python will serve as the primary development language, leveraging libraries such as OpenCV, PyTorch, and TensorFlow for modeling, while ensuring modularity and scalability for future enhancements. I will deliver a fully tested pipeline with clear documentation, ready to be deployed or integrated into your POS system. Could you share if the images come from fixed cameras or varying store layouts to optimize detection accuracy? • Ready to see why I'm the right fit for your project? Head over to my profile for client reviews and a glimpse of my portfolio: https://www.freelancer.com/u/ShaikhAneesa Lets chat and discuss further! Best Regards, Aneesa.

@quan9824

As an experienced full-stack developer with a unique blend of specialized skills in Artificial Intelligence, Computer Vision, Deep Learning, and Image Recognition, I am confident that I'm the best candidate to provide you with a precise and efficient ML-Powered POS Recognition System. I boast an impressively high 100% Job Completion Rate, having delivered all my projects on time while maintaining the best reviews. In line with the specific task requirements you've outlined, I have a solid understanding and extensive hands-on experience dealing with image recognition in various contexts. My familiarity with retail and shelf analysis would be pivotal in ensuring that not only can my system detect product name, category, price and face count from POS images, but also assess quality of exposition (height) effectively. Most importantly, the depth of my knowledge in AI extends beyond textbook learning to real-life problem-solving. From market basket analysis to sports score prediction and fraud detection to natural language processing; my portfolio is rich in practical projects that exhibit my versatility and capability to take on tasks similar to your project. Let's work together and revolutionize your point of sale analysis!

@teodorgeorge

Hello, I have experience developing machine learning systems focused on image recognition and retail analysis, which aligns well with your needs for a POS recognition system. I can build a solution using Python and computer vision techniques to detect product names, categories, prices, the number of product faces on shelves, and evaluate exposition quality based on height. My approach will include leveraging state-of-the-art ML models and tailoring them specifically for retail shelf image analysis to ensure accuracy and efficiency. Thanks, Teo

@Sidrairfan078

Hi there, I've thoroughly reviewed your project for the ML-Powered POS Recognition System, and I’m excited about the opportunity to help you create a robust solution. With my expertise in computer vision and machine learning, I've successfully implemented similar systems that recognize and analyze various products in retail settings. My extensive experience includes image recognition tasks where I’ve developed algorithms using Python and deep learning frameworks like YOLO, ensuring high accuracy in identifying product names, categories, prices, and more. I am confident that I can build a system that meets your requirements for recognizing packaged goods and assessing their shelf presence effectively. We can kick off the project promptly, and I anticipate needing around 15 days to deliver a working prototype.

@abhisaini0188

Hello, Thank you so much for posting this opportunity. It sounds like a great fit, and I’d love to be part of it! I’ve worked on similar projects before, and I’m confident I can bring real value to your project. I’m passionate about what I do and always aim to deliver work that’s not only high-quality but also makes things easier and smoother for my clients. Feel free to take a quick look at my profile to see some of the work I’ve done in the past. If it feels like a good match, I’d be happy to chat further about your project and how I can help bring it to life. I’m available to get started right away and will give this project my full attention from day one. Let’s connect and see how we can make this a success together! Looking forward to hearing from you soon. With Regards! Abhishek Saini

@efanntyo

Hello Hope you are doing well! This is Efan , I checked your project detail carefully. I am pretty much experienced with YOLO, Image Recognition, Python, React.js, Image Processing, Artificial Intelligence, AutoML, Computer Vision, Machine Learning (ML) and Deep Learning for over 8 years, I can update you shortly. Cheers Efan

@sekai1116

Hi, ✅ Nice to meet you here ✅ I can help you build a computer-vision–driven POS recognition system tailored for retail shelf analysis. Relevant experience & approach Computer Vision & ML: Strong background in object detection and classification using YOLO / Faster R-CNN / EfficientDet, combined with CNN-based classifiers for SKU recognition. Shelf analytics: Experience with planogram compliance, facings count, and shelf-position analysis (height-based exposure scoring). OCR & pricing: Use of OCR (Tesseract / PaddleOCR) and visual-price tag detection to extract and validate pricing. End-to-end pipeline: Image ingestion → preprocessing → detection → SKU matching → metrics computation → structured output (JSON/CSV/API). Python stack: PyTorch / TensorFlow, OpenCV, NumPy, Pandas; deployable as an API or batch processor. What I’d deliver Model(s) to identify product name & category Accurate facings count per SKU Exposure quality score based on shelf height and visibility Optional price extraction with confidence scoring Clean, documented code and reproducible training/inference pipeline I can share examples of similar retail image recognition and shelf analysis work and propose a realistic timeline once I understand your dataset size and image conditions (store type, camera angle, lighting). Happy to discuss details and get started.

@mykhaylob4

✋ Hi there. I can build your ML-powered POS recognition system to detect products, count shelf faces, and evaluate display quality accurately. ✔️ I have solid experience in computer vision and machine learning, including object detection, image classification, and retail shelf analysis using Python, OpenCV, and PyTorch/TensorFlow. In previous projects, I developed models to identify products in real-world store images, count stock units, and assess display layouts automatically with high accuracy. ✔️ For your project, I will design a pipeline that takes POS images as input, detects products, predicts their category and price, counts visible units, and evaluates shelf organization. I will optimize the model for speed and reliability while providing clean, modular Python code and documentation. ✔️ I will deliver a working system with example images, configurable parameters, and clear setup instructions so your team can run and extend it easily. Let’s discuss your image dataset, expected accuracy, and deployment preferences. Best regards, Mykhaylo

@ianb65

Hi, there! My name is Ian Brown, and I’d be happy to help with your project. I can provide a clean, reliable solution tailored to your needs, keeping everything simple, efficient, and easy to use. My goal is to streamline your workflow, save time, and deliver results that fit smoothly into your existing process. I’m ready to jump in and help make your project run as smoothly as possible!