Image Text Analysis With LLM-Guided Response Selection
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Solution Overview
Problem
Existing computing systems lack the ability to effectively analyze and extract valuable information from textual content in images and provide relevant services or actions in response to user requests.
Innovation Solution
A system that leverages image processing techniques and machine-learned models to extract textual content from images, determine characteristics, and provide services such as summaries, answers to queries, or explanations based on these characteristics, using a large language model to generate appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a computing system performs multiple different services or transformations on interesting components in images, then the system's functionality and adaptability are improved, but the device complexity increases
Solution Approach 1:
The computing system is designed to perform multiple different services or transformations on interesting components in images. The system can identify text, translate it, summarize it, extract entities, or perform other operations based on user selection, making it a multi-functional platform that handles diverse image processing tasks through a unified architecture.
Solution Approach 2:
The system dynamically adapts its behavior based on user input and characteristics of the extracted text. After extracting text from an image, the system presents multiple possible transformations (translation, summarization, entity extraction, etc.) and allows the user to select which service to apply, creating a dynamic workflow that adjusts to specific needs rather than following a fixed processing sequence.
2Productivity
If the computing system automatically determines and performs appropriate transformations on extracted text, then productivity is improved, but the extent of automation increases which may reduce user control
Solution Approach 1:
The system extracts text from an image, analyzes its characteristics, and then presents multiple possible transformations to the user for selection. This feedback loop allows the system to automatically perform the initial extraction and analysis, then seeks user input to determine the appropriate transformation, balancing automation with user control by involving the user at the decision-making stage rather than leaving the entire process automated or entirely manual.
Data Source
AI summary
The present disclosure provides computer-implemented methods, systems, and devices for responding to requests associated with an image. A computing system obtains, wherein the image depicts a first set of textual content. The computing system determines one or more characteristics of the first set of textual content. The computing system determines a response type from a plurality of response types based on the one or more characteristics. The computing system generates a model input, wherein the model input comprises data descriptive of the first set of textual content and a prompt associated with the response type. The computing system provides providing the model input as an input to a machine-learned language model. The computing system receives a second set of text as an output of the machine-learned language model as a result of the machine-learned language model processing the model input. The computing system provides the second set of text for display to a user, wherein the second set of textual content is associated with the response type.