Client:
Our Client - Florian Oswald, is an Assistant Professor of Economics at SciencePo university in France. He is a Ph.D. researcher with a focus on Urban and Macro Economics. His research utilizes computational techniques and modeling for structural estimations of economic models.
The Challenge
Florian is currently researching the structural changes to land usage in Urban cities. This required him to analyze France’s historical land usage patterns starting from the 18th Century.
He had to extract data like land usage and value from 140,000+ hand-written documents. These data were present in non-standard table format where the data from one column or row might intersect with another. Traditional OCR tools would be unable to capture the data from such a format, and it would take him months to process manually.
Florian required a user-friendly, robust, and accurate solution for extracting data!
The Solution
Nanonets AI is well equipped to automate the manual processing of documents in any format, font, and language. Florian could see instant results with little effort to train the AI model!
Nanonets AI has been trained with millions of documents and can extract data from unstructured document types. With some training, the AI could differentiate between intersecting rows and columns.
With Automated Workflows, they could place post-processing rules to identify errors and reduce validation time. They could get started in a day, making it easy for him to choose Nanonets.
I couldn't believe how easy it was to get started with Nanonets. And the results were incredible - I was able to extract data with over 95% accuracy in just 2 hours.