Let AI Instantly Parse Heavy Documents: The Magic of MPLUG-DOCOWL2’s Efficient Compression

Author(s): Florian June

Originally published on Towards AI.

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Today, let’s take a look at one of the latest developments in PDF Parsing and Document Intelligence.

In our digital age, the ability to understand documents beyond mere text extraction is crucial. Multi-page documents, such as legal contracts, scientific papers, and technical manuals, present unique challenges.

Traditional document understanding methods heavily rely on Optical Character Recognition (OCR) techniques, which present a significant challenge: the inefficiency and sluggish performance of current OCR-based solutions when processing high-resolution, multi-page documents.

These methods generate thousands of visual tokens for just a single page, leading to high computational costs and prolonged inference times. For example, InternVL 2 requires an average of 3,000 visual tokens to understand a single page, resulting in slow processing speeds.

Figure 1: (a) mPLUG-DocOwl2 achieves state-of-the-art Multi-page Document Understanding performance with faster inference speed and less GPU memory; (b-c) mPLUG-DocOwl2 is able to provide a detailed explanation containing the evidence page as well as the overall structure parsing of the document. Source: MPLUG-DOCOWL2.

As shown in Figure 1, a new study called MPLUG-DOCOWL2 (open-source code) aims to address this issue by drastically reducing the number of visual tokens while maintaining, or even enhancing, comprehension accuracy.

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Published via Towards AI