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 recognition, artificial intelligence and computer vision.
OCR has received 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 does exactly Clear Vison® help our clients?
- Minimizes time for data transfer from paper document to computer (ERP, CRM, etc.)
- Automatically recognizes characters and digits with high accuracy
- Operator needs only to verify the result of Clear Vision instead of manually entering data into the enterprise system
- Automatically sends data to preset fields of your enterprise system (ERP, CRM, etc.)
- High customization abilities to your particular business needs
- Can work offline/online and in Cloud
- Personal data protection ensured by encryption
- Eliminates human factor and its errors
Where can it be applied?
- For processing forms (questionnaires) that were filled out by hand
- For performing search through scanned documents in Electronic document management systems
- For marketing campaigns or any paperwork that an organization has
- For reading car number plates, street name plates, printed documents (passport, driving license, etc.)
- 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 on the promising approach of scientists and developers that uses cellular neural networks (hereinafter CNN). It uses the committees of CNN trained on different scale images. Our experiments at image processing show that designed classifiers are more effective than existing commercial systems.