Prompt-Guided Text Recognition via Knowledge Distillation
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Solution Overview
Problem
Legacy data records, especially those containing handwritten text, pose challenges for automatic decoding due to variability in shapes, fonts, and distortion, leading to limited accuracy in AI/ML systems, which struggle to identify text regions and decipher handwritten notes in complex documents like medical records.
Innovation Solution
A text recognition platform employing self-supervised, continual learning, and prompt-guided knowledge distillation techniques to preprocess images, determine regions of interest, and enhance text recognition by generating data augmentations, enabling robust detection of mixed-type and handwritten text.
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 dynamically adapts to different text characteristics by using attention mechanisms that weigh different regions and features based on their relevance. The model adjusts its processing focus dynamically based on the input document's specific properties, allowing it to handle variability in shapes, fonts, and character connectors effectively.
Solution Approach 2:
The system changes key parameters including attention weights, region of interest boundaries, and processing focus based on the specific input document. By dynamically adjusting these parameters rather than using fixed settings, the system achieves higher accuracy across diverse text formats and handwriting styles.
2Measurement precision
If supervised training is used to improve accuracy, then the system can detect specific text regions, but training data requirements become large and complex
Solution Approach 1:
The system performs preliminary unsupervised pre-training to learn general document structures and text region patterns before fine-tuning with supervised data. This preliminary learning phase allows the model to acquire foundational knowledge without requiring large amounts of labeled training data, reducing the overall training data requirements.
Solution Approach 2:
The system implements continual learning that allows the model to accumulate knowledge from both supervised and unsupervised sources continuously. Rather than requiring complete retraining with large datasets, the system integrates new information incrementally, maintaining accuracy while reducing the burden of training data collection.
3Measurement precision
If the system processes entire documents, then comprehensive text recognition is achieved, but processing time and computational resources increase
Solution Approach 1:
The system segments the document into multiple regions of interest based on detected text areas, separating them from non-text regions. By processing only the relevant segments rather than the entire document uniformly, the system achieves comprehensive text recognition while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on regions containing text, using attention mechanisms to identify and process relevant areas. This selective approach achieves comprehensive text recognition where needed while avoiding unnecessary processing of empty or non-text regions, optimizing the balance between completeness and efficiency.
Data Source
AI summary
A text recognition system receives a prompt and, based on the prompt, causes a trained region encoder to determine a first 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.