Selective Character Recognition in Multimodal AI Inputs
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Solution Overview
Problem
Multimodal AI models struggle with accurate text recognition from inputs that include documents with images, leading to inaccurate outputs and computational inefficiencies due to unnecessary character recognition processing.
Innovation Solution
A system determines whether to augment multimodal input with character recognition (CR) data based on predefined criteria, allowing the model to selectively use CR data to improve output accuracy and efficiency by avoiding unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If character recognition processing is applied to all multimodal inputs, then text recognition accuracy is improved, but computational resource usage increases
Solution Approach 1:
The system dynamically adjusts the application of character recognition processing based on real-time analysis of input characteristics. The multimodal model evaluates whether text is present and whether CR processing would be beneficial, making the processing pipeline adaptive rather than static. This resolves the contradiction by applying CR only when dynamically determined to be necessary, improving accuracy when needed while conserving resources when text is absent or already clear.
Solution Approach 2:
The system changes the processing parameter (application of character recognition) based on detected conditions in the multimodal input. By analyzing input characteristics such as the presence of text regions, image quality, and modalities involved, the system adjusts whether to apply CR processing, thereby optimizing the balance between recognition accuracy and computational resource consumption.
2Measurement precision
If character recognition processing is applied to all multimodal inputs, then text recognition accuracy is improved, but processing time increases
Solution Approach 1:
The system dynamically determines whether to apply character recognition based on input analysis, making the processing time variable rather than fixed. When text is clearly present and CR would help accuracy, the system applies CR and accepts the time cost. When text is absent or the input characteristics indicate CR would be unnecessary, the system skips CR processing, thereby reducing processing time and resolving the contradiction between accuracy improvement and time loss.
3Measurement precision
If character recognition data is always included in multimodal input, then model output accuracy is improved, but device complexity increases
Solution Approach 1:
The processing pipeline becomes dynamic with conditional branches that determine whether CR data generation and augmentation steps are executed. The system includes decision logic that analyzes multimodal inputs and selectively activates CR processing only when beneficial, thereby managing device complexity through intelligent control rather than always-on processing.
Solution Approach 2:
The system extracts and applies character recognition processing only to the specific subset of multimodal inputs where it would be beneficial, rather than applying it universally. By taking out the CR step from the mandatory processing chain and making it conditional, the system reduces overall pipeline complexity while maintaining accuracy where needed.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable storage media for determining whether to add character recognition (CR) data to multimodal input and executing models with multimodal input augmented with the generated CR data, to improve the execution or accuracy of output generated by the models. CR data is information describing the presence or characteristics of text across input of different modalities, such as video, images, or audio. The system can include a multimodal model trained to receive the multimodal input and generate a corresponding output, in response to the input, and can be trained to determine whether to include the CR data in the multimodal input. The determination of whether to use multimodal input augmented with CR data can improve the accuracy of a model output, the computational efficiency in processing multimodal input, or both.