Real-time Data Item Prediction Using Multi-model Video Analysis
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Solution Overview
Problem
Extracting data from electronic images, such as receipts, is challenging due to the complexity of accurately identifying and processing text data, especially when images contain handwritten or machine-generated text, and existing methods are inefficient and prone to errors.
Innovation Solution
A method involving an image capture device that captures video frames, performs object detection, and uses multiple machine learning models to predict candidate data items from text data, including OCR and text-to-token models, to generate and process text, and consolidate predictions based on confidence scores for accurate data extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning models are used to predict candidate data items, then data extraction accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the data extraction task by dividing it into multiple specialized machine learning models, each handling different aspects of text recognition and data prediction. This segmentation allows each model to focus on specific patterns, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The plurality of machine learning models serves multiple functions simultaneously: OCR for text recognition, prediction models for data extraction, and confidence scoring for validation. This multi-functionality approach consolidates what could be separate systems into an integrated pipeline, improving accuracy without proportionally increasing complexity.
2Loss of information
If video frames are processed instead of single images, then data extraction completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by capturing multiple video frames before final data extraction. This allows the object detector and machine learning models to work with accumulated visual information, improving data completeness. The preliminary frame capture and object detection create a robust foundation for subsequent processing.
Solution Approach 2:
The system processes video frames at selective intervals rather than continuously, applying partial action to balance completeness with efficiency. By processing key frames and using object detection to identify when to capture additional frames, the system achieves sufficient data completeness without the excessive time cost of processing every single frame.
3Reliability
If confidence score selection is used to choose predictions, then data reliability is improved, but processing complexity increases
Solution Approach 1:
The system implements feedback through confidence scoring, where each machine learning model outputs a confidence score that feeds into the selection process. This feedback mechanism automatically prioritizes high-confidence predictions and filters out uncertain ones, improving data reliability through a systematic rather than ad-hoc selection process.
Solution Approach 2:
The system changes the parameter of prediction evaluation from binary acceptance/rejection to continuous confidence scoring. This parameter transformation allows for nuanced selection based on confidence thresholds, improving reliability by systematically favoring high-confidence predictions while maintaining simple threshold-based decision logic.
Data Source
AI summary
Some embodiments provide a method that predicts data items from a real-world object in real-time. The method captures a video comprising a plurality of frames. The method further performs object detection on a frame in the plurality of frames to determine that the frame includes an object. The method also processes the frame using a plurality of models, wherein each model in the plurality of models is configured to predict a set of candidate data items associated with the object. The method selects one or more candidate data items from the sets of candidate data items associated with the object as a set of data items. The method populates a record with the set of data items.