LLM-Based Image Text Conversion for User Characteristics
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Solution Overview
Problem
Existing information processing systems struggle to effectively convert text from image data into formats suitable for individual users based on their characteristics, such as language, department, or age, which can lead to text that is not easily understood by the intended user.
Innovation Solution
The system includes circuitry that controls the reading of image data, acquires user characteristic information, and inputs instructions to a large language model to perform conversion processing, such as summarization or translation, ensuring the output is suitable for the user's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text is extracted from image data using conventional methods, then text extraction is achieved, but the converted text is not suitable for individual user characteristics
Solution Approach 1:
The system performs preliminary acquisition of user characteristic information (such as language preferences, department, age) before conducting the text conversion. This allows the large language model to be pre-configured with user-specific parameters, ensuring that the converted text is tailored to the user's comprehension level and preferences from the outset, rather than requiring post-processing adjustments.
Solution Approach 2:
The system applies different conversion parameters and processing approaches based on specific user characteristics. For example, text for younger users may use simpler vocabulary and explanations, while text for specialized departments may include domain-specific terminology. Each user receives customized text quality appropriate to their individual profile rather than a uniform conversion approach.
2Productivity
If conventional text conversion is performed without considering user characteristics, then conversion processing is completed, but comprehension by the intended user is reduced
Solution Approach 1:
The system dynamically adjusts conversion parameters such as language style, level of detail, terminology complexity, and sentence structure based on user characteristic information. The large language model receives modified prompts that incorporate user attributes, causing the output text parameters to automatically adapt to the target user's comprehension capabilities, thereby maintaining both efficiency and reliability.
3Adaptability or versatility
If a large language model is used with user characteristic information, then text conversion suitable for user characteristics is achieved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary information processing layer that receives user characteristic information and translates it into appropriate prompts for the large language model. This intermediary layer acts as a mediator between the raw user data and the AI model, organizing and formatting the characteristics into structured instructions that the model can effectively process, thereby managing system complexity while maintaining high adaptability.
Data Source
AI summary
An information processing system includes circuitry. The circuitry controls reading of image data from a document according to an instruction from a user. The circuitry acquires characteristic information of the user. The circuitry receives, from the user, information indicating conversion processing to be performed on text included in the image data. The circuitry extracts the text from the image data. The circuitry inputs, to a large language model, information including an instruction instructing that the conversion processing is to be performed on the text and that a result of the conversion processing is to be suitable for a person corresponding to the characteristic information. The circuitry acquires a conversion result that is output by the large language model. The circuitry outputs the conversion result.