Table Image Data Extraction Using OCR and ML Structure Recognition
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Solution Overview
Problem
Existing methods for extracting data from images, particularly tabular data, face challenges due to variability in image formats, misalignment, and difficulty in accurately recognizing table cells and relationships, leading to inaccuracies and time-consuming manual processes.
Innovation Solution
A data extraction service utilizing optical character recognition and multi-modal machine learning models to detect bounding boxes, classify characters, and construct digital data tables, capable of handling diverse image qualities and formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual conversion of data from image to digital form is used, then accuracy can be maintained through human judgment, but the process becomes time-consuming and unusable for large numbers of images
Solution Approach 1:
The patent replaces manual mechanical data extraction with an automated optical character recognition (OCR) system that uses machine learning models to detect text, identify table structures, and extract data from images. This substitution enables high-speed processing of large volumes of images while maintaining accuracy through trained algorithms that can recognize patterns and relationships in tabular data.
2Productivity
If hard-coded templates are used for data extraction, then processing speed can be improved, but the system cannot adapt to varying data formats and presentations across different images
Solution Approach 1:
The patent employs dynamic machine learning models that can adapt to varying data formats and presentations in images. Instead of static hard-coded templates, the system uses trained neural networks that learn patterns from training data and can generalize to handle different table structures, layouts, and formats, enabling both speed and adaptability.
Solution Approach 2:
The system changes parameters by using multiple machine learning models with different configurations optimized for various aspects of data extraction. The models can adjust their detection and recognition parameters based on the specific characteristics of each image, allowing the system to handle diverse formats while maintaining efficient processing.
3Speed
If traditional OCR methods are used, then processing can be performed quickly, but accuracy in recognizing specific characteristics like decimal points and single-character words deteriorates
Solution Approach 1:
The patent segments the data extraction process into multiple specialized machine learning models that each handle specific aspects: one model detects text regions, another recognizes characters, and additional models identify table structures and relationships. This segmentation allows each model to be optimized for its specific task, improving overall accuracy for challenging elements like decimal points and single-character words while maintaining processing speed.
Data Source
AI summary
A data extraction service is disclosed. The data extraction service may receive an image that includes a data table. The data extraction service may perform optical character recognition on the image. The data extraction service may label text in the image to identify the table. The data extraction service may generate a digital data table corresponding to the data table in the image. The data extraction service may format the digital data table and provide the formatted digital data table to a downstream entity.