Format-Agnostic Document Ingestion via OCR Text Elements
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Solution Overview
Problem
Conventional systems for ingesting documents of varying formats are fragile and require extensive training and upkeep, making them inefficient for handling multiple document formats, especially when scaling up to process large volumes of documents.
Innovation Solution
A format-agnostic document ingestion system that uses a processor to convert images of documents into text elements, identify document types, retrieve data detectors from a database, and validate text elements based on their content and position, allowing for the automatic ingestion of documents without prior knowledge of their format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use format-specific training to recognize document formats, then document type identification accuracy is improved, but system complexity and upkeep requirements increase significantly
Solution Approach 1:
The patent applies universality by creating a single document ingestion system that can handle multiple document formats without requiring separate trained models for each format. The system uses format-agnostic processing that works across different document types (invoices, receipts, bills, etc.) from various sources, eliminating the need for format-specific training while maintaining identification accuracy through a unified approach.
Solution Approach 2:
The patent introduces an intermediary layer between the document image and the processing system. This intermediary involves converting document images to text elements with positional information, then using a format-agnostic method to identify document types and extract data. This intermediary processing stage allows the system to handle diverse formats without direct format-specific training.
2Measurement precision
If conventional systems are trained on specific document formats, then processing accuracy for those formats is improved, but adaptability to new formats deteriorates
Solution Approach 1:
The system achieves universality by designing a format-agnostic document ingestion process that can process any document type without requiring retraining. The method uses general text element conversion and positional analysis that works across all document formats, allowing the system to maintain high processing accuracy while being highly adaptable to new formats from different sources.
Solution Approach 2:
The patent applies dynamics by making the document processing system flexible and adaptable rather than static and format-specific. The system dynamically adjusts to different document formats by using format-agnostic text element conversion and identification methods, allowing it to process new document types without retraining while maintaining accuracy through its adaptive processing approach.
3Adaptability or versatility
If manual document processing is used, then flexibility in handling different formats is improved, but productivity and scalability deteriorate
Solution Approach 1:
The patent applies self-service by creating an automated document ingestion system that handles format diversity independently without requiring manual configuration or intervention. The system automatically converts document images to text elements, identifies document types, and extracts data using format-agnostic methods, achieving both high productivity and flexibility without manual processing.
Solution Approach 2:
The system achieves universality by implementing an automated processing pipeline that can handle multiple document formats simultaneously. The format-agnostic approach allows the system to process invoices, receipts, bills, and other document types from various sources through a single automated workflow, achieving high productivity while maintaining the flexibility to handle diverse formats.
4Speed
If format-specific processing systems are implemented, then processing speed for known formats is improved, but loss of time for system preparation and upkeep increases
Solution Approach 1:
The patent applies self-service by creating a system that maintains itself without requiring external training or configuration. The format-agnostic document ingestion system automatically processes documents of any format without needing preparation time for training data collection, model training, or format configuration. This eliminates upkeep time while maintaining fast processing speeds through its inherently flexible architecture.
Solution Approach 2:
The system achieves universality by implementing a single processing pipeline that handles all document formats without requiring separate prepared systems for each format. This eliminates the time required to prepare and maintain multiple format-specific systems, achieving fast processing speeds across all formats through a unified, maintenance-free approach.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and scalable document ingestion across different formats, reducing the need for manual configuration and format-specific training, allowing organizations to work cooperatively without altering their systems or workflows.
Implementation Method 1
The processor is also configured to convert, using optical character recognition, the image of the document into a plurality of text elements
Data Source
AI summary
A system for format-agnostic document ingestion including a document ingestion server and a database is disclosed. The server is configured to receive an image of a document comprising text in an unknown format, convert the image, using OCR, into a plurality of text elements a content, a size, and an absolute position. The server is also configured to retrieve data detectors from the database, each associated with a data type anticipated to be in the document, and comprising at least one identifier and direction, and at least one validation criteria. The server is also configured to identify a potential descriptor by comparing the content of each text element with the at least one identifier, and then determine if the text element pointed to by the data detector meets the validation criteria. Finally, the server is configured to associate the validated text element with the data detector, and store the content.