Leading IT Company envisioned to Automate Data Processing from blueprints for Electrical Engineers
Trantor helped us create a solution that is by far one of the most reliable image and text processing tools on the market. Our customers are thrilled with the ease of use and cost reduction that this tool provides. Kudos to the Trantor team for their innovative approach to custom development.
Business problem
The client wanted to create a solution for electrical engineers, enabling them to derive meaningful data from electrical blueprints to enable informed cost decision-making.
Through its innovative solution, the client wanted to equip electrical engineers with meta-information on their blueprints at the click of a button. Existing solutions were limited to giving data using OCR tools. Such solutions gave
Limited Use of error-prone data. Also, the output data was dependent on input data quality and was, therefore, unreliable.
In-efficient process OCR tools only provided image capture and not data capture. Data still had to be typed in using manual, error-prone methods.
By itself, typed-in data with image capture could not provide any actionable information to the engineers.
Solution delivered
The team, after understanding the client’s process, architected a solution that would use Computer Vision and Machine learning to extract data. This data was further processed to provide actionable information
The team determined the steps required to provide actionable information from blueprints. Trantor chose Python as the software language. Keeping the blueprint in mind, they chose Computer vision and Machine learning in the backend to convert blueprints into Images and images into data. This data was further processed into structure format by using predefined rule sets.
Blueprints were converted into Images for Computer vision and Machine Learning(ML).
The processing flow was tailored to the characteristics of the source material
Using Computer Vision, the regions were detected in which the relevant data was present.
Machine learning was used for data extraction from detected regions.
Data were processed using built-in business rule sets
Processed data was moved into the server for easy retrieval and search;
Intuitive, user-friendly UI was designed and built to allow end-users to convert blueprints into actionable data in a few clicks
Results
30% improvement in bottom-line due to informed decision-making
60% reduction in operational cost
95% data accuracy
Python
ROR
Open CV
ML (KERAS)
Tesseract APIs
