Prompt-Guided Knowledge Distillation for Text Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy data records containing non-standard items like handwritten text are difficult to automatically decode due to variability in shapes, fonts, and character connectors, and existing AI/ML systems face challenges in accurately identifying text regions and deciphering handwritten notes, especially in mixed-type documents like medical records.
Innovation Solution
A text recognition platform that employs self-supervised, continual learning, and prompt-guided knowledge distillation techniques to preprocess images, detect regions of interest, and improve text recognition accuracy by generating data augmentations and using attention-based knowledge distillation to maintain knowledge without storing sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-existing AI/ML methods are used for text recognition, then the system can process documents, but accuracy is limited due to variability in shapes, fonts, and character connectors
Solution Approach 1:
The system transforms input images through multiple parameter changes including rotations, flips, scaling, and distortions to create augmented training data. This enables the model to learn invariant features across different orientations and transformations, improving accuracy for varied text formats while maintaining consistent performance
Solution Approach 2:
The system employs an ensemble of multiple pre-trained models (e.g., Tesseract, EasyOCR, PaddleOCR) that combine different recognition approaches. This composite model structure leverages the strengths of each individual model to achieve higher accuracy and reliability across diverse text types including handwritten and printed formats
2Measurement precision
If supervised training with large labeled datasets is used, then text recognition accuracy improves, but training requirements and resource needs increase
Solution Approach 1:
The system performs data augmentation as a preliminary action before training, generating transformed versions of existing labeled data through rotations, flips, and distortions. This preliminary preparation of augmented datasets enables effective training with smaller original datasets, reducing the quantity of required training data while maintaining high accuracy
Solution Approach 2:
The system creates multiple copies of existing labeled training data through augmentation transformations, generating synthetic training examples from limited original data. This copying approach with transformations enables the model to learn from diverse variations without requiring proportionally larger datasets
3Adaptability or versatility
If standard AI/ML systems are used, then general text processing is possible, but detection of specific text regions in mixed-type documents is difficult
Solution Approach 1:
The system segments the document processing task into distinct stages: first detecting text regions using object detection models, then classifying text types (handwritten vs. printed), and finally applying appropriate recognition methods. This segmentation enables specialized handling of different text regions within mixed-type documents while maintaining manageable system complexity through modular architecture
Data Source
AI summary
A text recognition system causes a trained region encoder to determine a region of interest of an image file. The system modifies a first image associated with the first region of interest (e.g., parsed out from the first region) to generate a data augmentation entity that includes a modified image. Using a trained instance encoder, the system generates a first set of visual instances corresponding to the first region of interest image and a second set of visual instances corresponding to the data augmentation entity. The system generates the corresponding first and second sequences. By executing a self-supervised contrastive loss function on the first and second sequences, the system automatically updates a continual knowledge distillation model of the trained region encoder. The system provides the first sequence to an instance decoder to generate output text in response to the prompt.