Recent News and Events
CVISION Technologies Inc., a leading provider of advanced document capture software, announced today that it will provide an international university based in China with an advanced OCR solution to improve the accessibility of its image documents. The university purchased a license of Maestro Recognition Server to process an unlimited number of pages.

The university had a large number of documents that had been scanned as TIFFs in their databases. These TIFFs were not text searchable, which made it difficult to locate information within them. To facilitate an improved search experience, the university looked for an OCR solution that could accurately convert a large batch of files. After researching a variety of different software brands, the university came to the conclusion that the Maestro Recognition Server from CVISION Technologies was the ideal solution.

The Maestro Recognition Server is a software solution that can improve the accessibility of image documents. Its OCR is one of the most accurate recognition engines available in the market and consistently delivers effective results. The Maestro Recognition Server was able to batch convert all of the university’s TIFFs into fully text searchable files. With these newly accessible files, the university saw a significant improvement in its search times.

Chris Koulouris, Marketing Director at CVISION Technologies, said, “China is a rapidly emerging market and we at CVISION are excited at the opportunity to work with an eminent university based there. We were able to provide them with an effective OCR solution and hope there will be more chances to work there in the future.”

About CVISION
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.