THE IDEA
Data Gathering is the most crucial step in image recognition which deals with collecting information about the facts to be analysed.
And hence, Image recognition is widely used in retail for various usecases.
Xplorazzi combines computer vision and deep learning technology to recognise Retail SKUs from an image on an SKU level which helps
retailers and CPG Brands take actions on premises to drive sales. Xplorazzi recognition engine can detect retail items with
90%+ accuracy which helps to automate business lines.
THE INGENUITY
Technology Behind This
The process of sku recognition goes through 5 phases
- Image capture: Collecting images from shelf cameras, Kiosk Cameras, or mobile phones.
- Image preprocessing: Images usually need to be pre-processed to reduce noise and redundant information to provide high-quality images for further processes. It mainly includes image segmentation, transformation, and enhancement.
- Feature extraction: the analysis and processing of image data to determine the invariant characteristics in the image.
- Feature classification: after a certain image feature is mapped to the feature vector or space, a specific decision rule is applied to classify the low-dimensional feature to make the recognition result accurate.
- Recognition output: the pre-trained network is employed to predict the category/brand/sub-brand of the retail product.
The core of product recognition are Convolutional Neural Networks (CNNs). The purpose of deep learning is to learn deep representation, i.e., to learn multilevel representation and abstraction from images. The Deep learning training needs annotated images with bounding boxes to be fed. The accuracy of SKU detection is highly dependent on the size of data sets with all possible variations in data which is possible for certain situations on the retail ground including occlusion, daylight, evening light, surrounding items, etc.
Deep learning for SKU recognition requires a large amount of annotated data for training which creates heavy workload for a data engineer. To solve that, Xplorazzi’s propriety engine generates synthetic data that is mixed with a few real-world images to be utilized to train deep learning networks so that engine can identify and locate the Retail SKUs with good accuracy. Xplorazzi synthetic data generation is specialized for retail and generates 1000s of annotated images in a few minutes.
HOW DO WE ONBOARD SKUs
Process flow on how to onboard SKUs faster
Onboarding app to click product
specific images from client end
End to end automated
pipeline for AI Training
Use of Synthetic Data to reduce the
manual effort of image annotations
DOCKER BASED DEPLOYMENT
Deployment of AI Recognition Model
On Cloud
On Prem
INTEGRATION
Process flow on how to intergrate our SKU recognition Engine
REST API Integration
Simple 4 REST APIs integration to recognize SKUs from an image. The REST APIs can be called either from mobile applications or directly from servers. The API Documentation clarifies all the steps and processes that need to be followed by the developers to take care of.
Get API DocumentationSAP Integration
Our modules are integrated with SAP business modules to give a seamless integration experience for the existing SAP customers. The Architecture documentation explains all the version of SAP module has been used along with processes.
Get API Documentation
