Success Story - en

Recognition of checks and bills

Motivation for launching the project by the customer:
the need to scale the customer’s business revealed limitations in the current solution: the business model of the solution used to automate document flow using humans had a high cost and low scaling capabilities.

Description of the initial situation:
  • High efficiency of document flow with a minimum number of errors involves storing electronic versions of documents and rapid digitization of printed versions of documents.
  • Processing of printed versions of documents involves converting images into an electronic structured representation with high accuracy.
  • The business process used for this is not suitable for scaling, since manual labor is used to recognize printed versions of documents.
  • For this reason, there were significant risks for the planned business growth.

Project goals:
  • creation of a system for recognizing the main fields of documents with a sufficient level of quality
  • creation of a tool for generating new document formats.

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.

Solutions for recognition automation have been built:
Checks and receipts;
Work order;

To build the model we used:
  • Marked up document template;
  • Document reference fields with their characteristics;
  • Set of document images.

Simulation results:
  • OCR character extraction model;
  • Model for searching text blocks Text Detection;
  • Table Detection model;
  • Model for constructing an electronic version of a document.

Customer: Finance, Accounting
Technology stack: TensorFlow, Python, Flask.
Computer Vision OCR Research Engineering
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