Foxit Cvision PDF Compressor

Foxit PDF Compressor is an OCR server equipped with enhanced compression that can dramatically reduce the size of PDF files. This can lead to big cost savings in cloud storage and bandwidth fees, and improved efficiency for knowledge workers who save time on every file they open.

5 Tips for Selecting a Document Compression Solution

Pricing starts at $375 for 12,000 pages per year. One-time and CPU-based licensing is also available. Contact us for pricing and bundling options for any page volume.

Description

Foxit Compressor is an OCR server equipped with enhanced compression that can dramatically reduce the size of files. This can lead to big cost savings in cloud storage and bandwidth fees, and improved efficiency for knowledge workers who save time on every file they open.

5 Tips for Selecting a Document Compression Solution

PDF Compressor uses the OmniPage , proving incredibly fast recognition speeds and high accuracy in over 200 recognition languages.

In most cases, making a PDF searchable with OCR will increase the size of the files, sometimes significantly. Many OCR applications will even convert text-based PDF files to images before making them searchable which can make them up to 10 times bigger!

PDF Compressor intelligently applies MRC, JPEG2000 and JBIG compression algorithms to the parts of the document that can achieve the biggest reduction from each. Color images, text and backgrounds are separated and compressed individually. Other OCR applications will only apply one compression type for the whole document, which can reduce the quality of mixed documents while it fails to achieve optimal compression.

PDF Compressor will also convert other electronic documents like Word, PowerPoint, Excel, HTML and others to highly compressed PDF files. This standardizes the output format for archival and makes them easily searchable and accessible across multiple platforms.

Use PDF Compressor with an indexing tool like SimpleIndex to automatically rename and organize OCR'd PDF files into folders based on metadata extracted from the text.

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