AI Document Page Classification and Targeted Data Extraction
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Solution Overview
Problem
Existing content management systems face challenges in efficiently searching and extracting specific information from unstructured digital assets due to unsearchable file formats, difficulty in finding specific pages within large documents, time-consuming manual metadata extraction, incorrect OCR results from improper page orientation, and human errors in classification and data extraction.
Innovation Solution
A system utilizing deep learning and AI for automated document page classification and targeted data extraction, which trains models to identify document types based on page layout and features, allowing for the extraction of relevant text areas through OCR processing without requiring text presence, and uses multiple models to validate predictions and prevent false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to find specific pages and extract metadata, then human judgment and adaptability are maintained, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical processes of document navigation and metadata extraction with an automated AI-based system. The system uses machine learning models to automatically identify document types, locate specific pages, and extract metadata without human intervention, thereby eliminating time-consuming manual operations while maintaining accuracy through trained neural networks.
Solution Approach 2:
The system enables self-service automation where the AI model independently performs document classification, page location, and metadata extraction tasks. The trained neural network automatically processes documents without requiring human operators to manually navigate through documents or extract information, allowing the system to serve itself in performing these routine tasks.
2Measurement precision
If traditional OCR is applied to all pages, then complete text extraction is achieved, but incorrect results occur due to improper page orientation
Solution Approach 1:
The patent applies preliminary action by first classifying document types and identifying the correct page orientation before applying OCR processing. The system uses machine learning models to pre-analyze the document structure, determine the proper reading direction, and orient pages correctly before text extraction begins, preventing OCR errors caused by misoriented pages.
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously monitors and validates OCR results against the classified document type and detected page orientation. If inconsistencies are detected, the system can re-process the page with corrected orientation or flag the extraction for review, ensuring high accuracy in the final extracted text.
3Reliability
If human classification methods are used, then contextual understanding is maintained, but errors and inconsistencies increase
Solution Approach 1:
The patent replaces human classification judgment with an automated machine learning-based classification system. The neural network models are trained to automatically identify document types, formats, and structures without human intervention, providing consistent and reliable classification results while processing large volumes of documents that would be impractical for manual classification.
4Adaptability or versatility
If deep learning models are trained on diverse document types, then adaptability to different formats is improved, but system complexity increases
Solution Approach 1:
The patent implements universality by developing a multi-functional AI system that can handle diverse document types through a unified architecture. The machine learning models are designed to classify and process various document formats (invoices, contracts, forms, etc.) using shared neural network components and common processing pipelines, reducing overall system complexity compared to separate specialized systems for each document type.
Data Source
AI summary
A system and method for automated document page classification and targeted data extraction. A method for identifying document page types using deep learning (Artificial Intelligence and Machine Learning), and page classification, based on trained models. The layout of the page, as well as where the features are on a given page, from which text is to be extracted are trained on (with human input guiding the construction) and stored in these models. Different types of pages, including text and images, could then be stored in these models, which can then be used for identifying the content on each page to look for the desired feature from which to extract text. Based on a page prediction, the solution then uses the appropriate pre-trained feature extraction model (if one exists) to extract the areas of interest for further OCR processing (retrieving the text).