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%