Springs

Warehouse ML Analyser

About Project
Springs team developed a tailor-made solution for the optimization of receiving processes on the large transshipping warehouse for the logistics provider.

We created a web app using Javascript (NodeJS & ReactJS) and connected an ML-based engine for image and text recognition using Python & PyTorch.
Client's goal
The main focus of the client was to improve the processing of incoming packaged goods before further distribution through minimization of necessary time and human resources. Their company suffered from tons of manual work and they were looking for ML automatization of those processes.
About Project
Springs team developed a tailor-made solution for the optimization of receiving processes on the large transshipping warehouse for the logistics provider.

We created a web app using Javascript (NodeJS & ReactJS) and connected an ML-based engine for image and text recognition using Python & PyTorch.
Client's goal
The main focus of the client was to improve the processing of incoming packaged goods before further distribution through minimization of necessary time and human resources. Their company suffered from tons of manual work and they were looking for ML automatization of those processes.
Product
The product core is a custom WMS module that was developed to take over the performance of the following tasks using cameras (without the fish-eye effect) mounted across the facility:
- label recognition and processing;
- generation and printing of unified labels.
Result
The integration of the module allowed minimizing expenses and risks concerning proper goods receiving and forwarding. The system implementation became possible after the gathering of the required dataset for machine learning to enable high-accuracy recognition of marking text and the following search for a match in the database.
Just take a look

Desktop

Technologies
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01React
02Node Js
03Mongo DB
04Python
05PyTorch
06Tensorflow
07GraphQL