Discover how document intelligence revolutionizes AI workloads by automating pattern recognition in bounding box text and receipt processing. Learn how this cutting-edge technology enhances accuracy and efficiency in data handling.
Table of Contents
Question
Which type of AI workload involves the automatic recognition of patterns in bounding box text and coordinate locations found on receipts?
A. Knowledge mining
B. Recommendation systems
C. Document intelligence
D. Conversational AI
Answer
C. Document intelligence
Explanation
Document intelligence is the AI workload that recognizes patterns in bounding box text and coordinates their locations, enabling the extraction of information from documents. It is a process that uses machine learning models to recognize and analyze text data. This includes extracting text, layout, and key-value pairs and identifying text locations on a page using bounding box coordinates. The example of a receipt illustrates how information is stored as key-value pairs with corresponding coordinates. This allows an AI system to store information such as an address on a receipt as key-value pairs with corresponding coordinates. Each bounding box is assigned a set of coordinates, typically four numbers like [4.1, 2.2], [4.3, 2.2], [4.3, 2.4], [4.1, 2.4]. These numbers represent the X and Y positions of the box’s corners on the digital image of the receipt. Document intelligence uses these bounding boxes and coordinates to identify text relevant to the address. It can analyze the location and content of the text within these boxes. For example, it might recognize that a box containing “123 Main Street” has coordinates placing it below a box containing “Your Company,” suggesting they’re part of the same address.
Knowledge mining does not involve the automatic recognition of patterns in bounding box text and coordinate locations. It focuses on extracting insights and relationships from unstructured data. This data can come from various sources such as social media posts, customer reviews, or sensor readings. The goal is to uncover hidden patterns, trends, and connections within this data to gain valuable knowledge.
Conversational AI does not involve the automatic recognition of patterns in bounding box text and coordinate locations. This technology focuses on human-machine interaction through natural language, not analyzing text within specific formats such as receipts.
Recommendation systems do not involve the automatic recognition of patterns in bounding box text and coordinate locations. Instead, they focus on recommending items to users based on various aspects of their user data, such as:
- Purchase history: What items have users bought or viewed in the past?
- Search history: What items have users searched for?
- Ratings and reviews: How have users rated or reviewed items?
- Demographic information: Age, location, gender (if applicable)
- Implicit feedback: Click-through rates, time spent on product page
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