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CVISION Provides Marketing Firm Solution to Optimize Image Documents

CVISION Technologies Inc. (, a leading provider of advanced document capture software, announced today that it will provide a marketing firm with a solution to optimize image documents. The firm leased a license of Maestro Recognition Server to process an unlimited number of pages.

The marketing firm provides marketing services such as advertising, website development, and digital design to other companies. In their work the firm has accumulated a large number of image documents which need to be accessed. However because the documents were not text searchable it took a considerable amount of time to locate information within them. As a result the firm looked for an OCR engine that could convert their image documents into searchable files. They found a solution in the form of Maestro Recognition Server from CVISION Technologies. 

Maestro Recognition Server is OCR software that is capable of converting image documents into machine-encoded, text searchable and editable files. Maestro is equipped with one of the most accurate recognition engines in the market, allowing for highly accurate file conversions. Once a document is searchable it becomes more accessible and efficient to work with. For increased utility, Maestro is also able to recognize different languages and perform powerful batch processing.

Chris Koulouris, Marketing Director at CVISION Technologies, said, “OCR is a very compelling solution for many companies because it improves the accessibility of their documents. By optimizing their document workload companies can experience a substantial improvement in their business processes.”

CVISION Technologies Inc. creates software to optimize the accessibility and processes associated with document capture. CVISION offers the best in file compression, recognition technology, automated data extraction, and PDF workflow applications. Through more efficient, automated workflows, CVISION clients can realize a measurable return on their document-driven processes.