With IBM Watson Discovery, you can boost the productivity of knowledge workers by automating the discovery of information and insights with advanced Natural Language Processing and Understanding. That means faster business results, satisfied customers and happier employees.
Accelerate business decisions and processes with an intelligent document understanding.
Accelerate decisions and processes
Generate revenue
Reduce costs
Provides user friendly tooling
Smart Document Understanding (SDU) is a visual machine learning tool that enables users to label text so that the tool builds an understanding of critical components inside your enterprise documents, such as headers, tables and more. Once you annotate a few pages of your documents, SDU can teach itself the rest, retrieving answers and information only from relevant content.
IBM’s new OCR provides higher character accuracy and detection recall giving you greater extraction of key insights from documents in sub-optimal conditions such as irregular fonts, insufficient resolution, bad lighting from mobile capture and even variety of colors and backgrounds.
Traditional enterprise search engines perform keyword searches and provide end-users with links to documentation. But with Watsonx Discovery, you move beyond keyword search to navigate confidently through your documents using faceted search to find the relevant information needed. The design of this platform ensures that the information you need is easily accessible and can push to downstream automation via JSON output.
The platform’s out-of-the-box NLP enrichments include entity extraction, sentiment analysis, emotion analysis, keyword extractions, category classification, concept tagging and more.
IBM Watson Discovery uses foundation models from Watson Natural Language Processing (NLP) for advanced processing and understanding of text, enabling the accurate extraction of information and insights from business documents, accelerating processes and generating insights.
Define custom entity types and annotate entity mentions to iteratively train a supervised entity recognition model with active learning techniques to accelerate training.
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