Neural Network Tabular Data Extraction from Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automatic content recognition systems face challenges in deciphering tabular data presented in various layouts, styles, and formats without explicit structural information, making it difficult to extract meaningful insights from literature and documents.
Innovation Solution
A computer-implemented method using machine learning models, specifically training neural networks to delineate and extract tabular data from images by identifying discrete columns, rows, and cells, and extracting content without requiring explicit markup or structural descriptions, utilizing a deep learning architecture with convolutional neural networks and recurrent neural networks for structure and content decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content recognition systems are used to decipher tabular data, then the systems can process documents with explicit structural information, but they fail to accurately decipher tabular data presented in various layouts and styles without markup data
Solution Approach 1:
The neural network model is designed to perform multiple functions: it can process both marked and unmarked tabular data, handle various layouts and styles, and adapt to different document types. This universal approach resolves the contradiction by making the system versatile enough to handle diverse tabular formats while maintaining high accuracy through unified processing mechanisms.
Solution Approach 2:
Markup data serves as an intermediary element that bridges the gap between visual tabular data and structured information. The system uses markup data during training to learn the relationship between visual layouts and tabular structures, enabling it to accurately decipher both marked and unmarked tables without requiring explicit structural information at inference time.
2Measurement precision
If manual markup data is provided for every tabular data, then the structure can be accurately identified, but the process becomes time-consuming and requires human intervention
Solution Approach 1:
Markup data is prepared in advance as training data to teach the neural network model how to identify tabular structures. Once trained, the model can automatically process new tabular data without requiring manual markup, eliminating the time-consuming process of preparing markup for each new table while maintaining high accuracy through pre-learned patterns.
Solution Approach 2:
The neural network model performs self-service by automatically learning to identify tabular structures from training data and then autonomously processing new tabular data without human intervention. The system serves itself by using the training phase to acquire the capability of structure identification, which it then applies independently to new inputs.
3Productivity
If simple content extraction is used, then the process is fast, but the system cannot understand the structure of tabular data without explicit descriptions
Solution Approach 1:
The tabular data processing is segmented into distinct components: structure identification (detecting rows, columns, and cells) and content extraction (reading text from identified cells). The neural network model performs structure identification first to understand the tabular layout, then extracts content from the identified structure. This segmentation allows the system to maintain high speed while preserving structural information understanding.
Data Source
AI summary
A computer-implemented method for using a machine learning model to automatically extract tabular data from an image includes receiving a set of images of tabular data and a set of markup data corresponding respectively to the images of tabular data. The method further includes training a first neural network to delineate the tabular data into cells using the markup data, and training a second neural network to determine content of the cells in the tabular data using the markup data. The method further includes, upon receiving an input image containing a first tabular data without any markup data, generating an electronic output corresponding to the first tabular data by determining the structure of the first tabular data using the first neural network and extracting content of the first tabular data using the second neural network.