Success Story - en

ID document recognition

Motivation for the customer to launch the project:
the need to scale the customer’s business revealed limitations in the current solution: the business model of the product used did not allow reducing the cost of the solution when scaling.

Description of the initial situation:
  • checking a client’s creditworthiness when issuing a bank loan involves;
  • recognizing his personal identification documents using semi-automatic and automatic methods;
  • reducing the risk of incorrect document recognition is ensured by high-quality cross-checking of documents by operators;
  • the manual and automated solutions used had scaling limitations for this reason, there were significant risks for the planned business growth.

Project goals:
  • creation of a recognition model for the main fields of identification documents with a sufficient level of quality;
  • creating a model for a fixed cost, which will avoid an inappropriate business model (transactional) when scaling.

MIL Team solution:
the use of the team’s existing solutions in the field of detection and recognition of text in images based on neural network models made it possible to quickly implement a solution with the required level of quality.

To build the model we used:
  • A set of images and scans of personal documents;
  • Marking images with text boxes;
  • The true value of each field;
  • Database of named entities of the Russian Federation.

Simulation results:
  • Model for recognizing the main elements of a passport;
  • Character recognition models for each passport element.

Full name recognition accuracy >85%
Gender recognition accuracy >88%
Recognition accuracy of license plates and series >91%
Accuracy of place and date of birth recognition >73%
Accuracy of recognition of the Authority and date of issue >80%

Customer: Finance, Banking, Insurance
Technology stack: OCR, TensorFlow, Python.

MVP Lab Computer Vision OCR Engineering Research
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