Document Data Extraction System Using OCR and Machine Learning
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Solution Overview
Problem
Document data processing is time-consuming and prone to errors, especially when manual, and existing automated systems require strict document templates and large datasets for effective machine learning classification.
Innovation Solution
A document data extraction system using optical character recognition (OCR) to extract metadata from document images and machine learning logic to determine key-value pairs, allowing for flexible document processing with a small training dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used, then flexibility in processing diverse documents is maintained, but processing time increases and error rates increase
Solution Approach 1:
The patent replaces manual mechanical data entry with an automated system combining OCR (optical character recognition) and machine learning classification. The OCR component converts document images to text, while the machine learning model automatically classifies and extracts key-value pairs, eliminating human intervention and thereby increasing both speed and consistency.
Solution Approach 2:
The system enables documents to be processed autonomously without human assistance. The machine learning model is trained to independently identify document types, extract relevant fields, and output structured data, making the entire workflow self-service and eliminating the need for manual data entry personnel.
2Productivity
If automated OCR and machine learning classification are used, then processing speed increases, but computing power and time requirements increase
Solution Approach 1:
The system performs preliminary OCR processing to convert document images into text before classification. This pre-processing step extracts raw text content that can be more efficiently analyzed by the machine learning model, reducing the computational burden during the classification phase compared to analyzing raw images directly.
Solution Approach 2:
The processing system is divided into distinct modular components: OCR logic for text extraction, machine learning logic for classification, and post-processing for key-value pair extraction. This segmentation allows each component to specialize in its specific task, optimizing computational efficiency and enabling parallel processing where applicable.
3Measurement precision
If strict document templates are required for automated processing, then processing accuracy improves, but adaptability to diverse document formats decreases
Solution Approach 1:
The machine learning model is trained on diverse document samples with varying formats, layouts, and styles. By learning from this varied training data, the model adapts to different document types (invoices, receipts, forms, etc.) and can accurately extract key-value pairs regardless of specific format variations, eliminating the need for strict template matching.
Solution Approach 2:
The system employs a universal machine learning classification model that can handle multiple document types and formats through a single unified approach. Rather than requiring separate processing pipelines for different document formats, the model generalizes across various document structures, making the system versatile and adaptable to diverse inputs.
Data Source
AI summary
A method performed by a computing system includes receiving, by a document data extraction system (DDES), image data associated with a document. The DDES extracts, via optical character recognition (OCR) logic of the DDES, metadata from the image data. The metadata specifies sequences of text content items and text content item features associated with each text content item of the sequences of text content items. A machine learning logic (MLL) module of the DDES determines, based on the sequences of text content items and the text content item features, one or more text content items associated with a key. The DDES communicates information that specifies the key and a corresponding value that is associated with the one or more text content items that are associated with the key to a terminal.