Optical character recognition (OCR)

Optical character recognition (OCR) is the mechanical or electronic conversion of images of typewritten or printed text into machine-encoded text. It is widely used as a form of data entry from printed documents, whether passport, driving license, invoices, receipts, business card or other documents. This common method of digitizing characters is used to enable editing, search, storage, display and used of paper documents. OCR is a field of research in pattern recognitionartificial intelligence and computer vision.

OCR has received an increased attention among developers and scientists from the early age of machine vision. This is due to many fields of its potential application i.e. practical motivation. Recognition is one of the classical problems in the context of artificial intelligence.

What is Clear Vision®?

A smarter way to automate your data input and document processing.

How exactly Clear Vison® helps our clients?

  • Minimize time for data transfer from paper document to computer (ERP, CRM, etc.)
  • Automatically recognize characters and digits with high accuracy
  • Operator needs only verify the result of Clear Vision instead of manually entering data into the enterprise system
  • Automatically send data to preset fields of your enterprise system (ERP, CRM, etc.)
  • High customization abilities to your particular business needs
  • Can work on offline/online and in Cloud
  • Personal data protection by encryption
  • Eliminate human factor and its errors

Where it can be applied?

  1. For processing forms (questionnaires) filled out in hand
  2. For performing search through scanned documents in Electronic document management systems
  3. For marketing campaigns or any organization paper work
  4. For reading car registration plates, street name plates, printed documents (passport, driving license, etc.)
  5. In such areas as banking and finance, education, medicine, telecom, government and many other

What is Clear Vison® based on?

The Solution’s core is based at promising approach of scientists and developers that uses cellular neural networks (hereinafter CNN). It uses the committees of CNN trained on different scale images. Empirical data tested on conventional printed text and typewritten image bases show that designed classifiers are more effective than existing commercial systems.

How accurate is CNN core?

Tests on MNist (database of handwritten digits by Mixed National Institute of Standards and Technology) showed the 2nd result in the world – 99.64%.

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