LatestMachine LearningLet AI Instantly Parse Heavy Documents: The Magic of MPLUG-DOCOWL2s Efficient Compression 0 like November 13, 2024Share this postAuthor(s): Florian June Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Today, lets 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.A Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post