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Image recognition for product identification

RS Components – Image Recognition

RS Components, the number one high-service distributor of electronics and maintenance products across Europe and Asia Pacific.

RS Components, the number one high-service distributor of electronics and maintenance products across Europe and Asia Pacific.

Task

Create an image recognition framework that can form the basis of product identification services across a range departments, tools and services offered by RS Components. Enabling their long-term strategic goal to be the market leader in customer experience and innovation.

How we did it

H&C partnered our machine learning and image recognition experts with the value added services team and sales team to form a comprehensive taskforce.

Collaboration

We held a workshop for the entire taskforce and discussed different approaches and data requirements. After reaching a collaborative consensus, we established initial user stories to guide the development process.

Build

We designed and built an image recognition system and accompanying prototype self-service kiosk with imaging capabilities. These then went through a series of iterative stages to refine and improve, all the while building on user feedback. A staged rollout to the market gathered early user feedback and generated additional sample images which were used for further training.

Tech

Product identification using imagery differs from conventional software, by requiring a two-stage development approach of training followed by runtime-operation.

Developing product identification system by using images is a hard problem to solve. It’s particularly challenging when classifying user generated images taken under random conditions. To achieve more accurate classification we undertook a multifaceted approach. First, we enlarged the training data set of images, by applying transformations which mirror common photo errors, including being out of focus, unwanted shadows, distortions etc. This data set was then added to the original, and we used it as a basis for training the image recognition neural nets.

We then developed a multi-layered image recognition framework which utilised TensorFlow and convolutional neural networks. This base capability was then supplemented with the addition of bespoke colour extraction and object size detection layers, before applying a final OCR detection layer.

By utilising these multiple layers within the image recognition system, we were able to significantly increase the capability and accuracy that the system achieved.

In addition, the image recognition system has been designed as a self-learning system. Which enables it to use each and every interaction to expand its training set, making the system smarter and more accurate as more customers use it.

Impact 

The image recognition system aims to increase sales by providing faster and more accurate identification of unknown products. Allowing customers to easily purchase the products they need in a more efficient and automated process.

Methodology RS-ImageRecog Created with Sketch. Learning Improvements Improvements AI Image Recognition UX Improvements CONTINUOUS DELIVERY Machine Learning Image Enhancement APIs System AI Image Recognition ALPHA Hack and Craft RS Team Team members PROJECT ROADMAP 3. Scale 2. Development 1. Ideation
Dev RS-ImageRecog Created with Sketch. SEARCH RESULTS PREDICTION ATTRIBUTES & VALUES PRODUCT PREDICTION PRODUCT CATEGORIES AI & MACHINE LEARNING & IMAGE RECOGNITION REMOVAL IMAGE BACKGROUND EXTRACTION IMAGE COLOUR SIZE DETECTION PRODUCT TEXT DETECTION OCR LIGHTBOX DESIGNED IN PURPOSELY IMAGES TAKEN PRODUCT IMAGES USER GENERATED TRAINING IMAGES PRODUCT INFORMATION PRODUCT RS Components

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Outro

Science and technology are the principal drivers of human progress. The creation of technology is hindered by many problems including cost, access to expertise, counter productive attitudes to risk, and lack of iterative multi-disciplinary collaboration. We believe that the failure of technology to properly empower organisations is due to a misunderstanding of the nature of the software creation process, and a mismatch between that process and the organisational structures that often surround it.