OCR-Free Document Information Extraction for Variable Layouts
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Solution Overview
Problem
Existing methods for processing unstructured image-based documents, such as purchase orders, face challenges including high computational costs, inflexibility, error propagation, and poor generalization due to variability in skewness, image quality, and text agglomeration, making it difficult to extract key data efficiently and accurately.
Innovation Solution
An OCR-free end-to-end neural network architecture using a transformer-based encoder, decoder, and parser to directly map raw image-based assets into structured output, trained through cross-entropy loss and fine-tuning with real document images, enabling direct extraction of relevant features and understanding of complex layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If OCR-based methods are used for information extraction from image-based documents, then text recognition capability is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent extracts and processes only the most salient visual features and text regions from document images, rather than performing full OCR processing on the entire image. This selective extraction approach maintains text recognition accuracy while significantly reducing computational overhead and processing time.
Solution Approach 2:
The patent segments the document processing task into distinct stages: visual feature extraction, text region identification, and information extraction. By dividing the processing pipeline and applying different processing intensities to different segments, the system achieves accurate text recognition only where needed, reducing overall computational cost.
2Productivity
If traditional document processing methods are used, then processing capability is improved, but flexibility and adaptability to different document layouts decrease
Solution Approach 1:
The patent implements a dynamic processing framework that adapts to different document layouts and structures. The system automatically adjusts its processing strategy based on the detected document type, layout complexity, and content distribution, enabling high productivity across diverse document formats while maintaining flexibility.
Solution Approach 2:
The patent creates a universal information extraction framework that can handle multiple document types (invoices, receipts, forms, etc.) and languages through a single system. The visual feature extraction and text region identification components are designed to be layout-agnostic, providing adaptability across different document structures while maintaining high processing capability.
3Measurement precision
If OCR processing is applied to handle various document conditions, then text extraction accuracy is improved, but computational resources and complexity increase
Solution Approach 1:
The patent applies different processing qualities and intensities to different regions of the document image based on their importance and characteristics. High-precision text recognition is applied only to text-bearing regions, while visual feature extraction uses lower computational resources for non-text areas, thereby maintaining extraction accuracy while reducing overall system complexity.
Solution Approach 2:
The patent replaces traditional mechanical OCR processing with a vision-based information extraction approach that uses visual feature extraction and pattern recognition. This substitution eliminates the need for complex OCR engines and associated preprocessing steps, reducing system complexity while maintaining or improving extraction accuracy through direct visual understanding.
Data Source
AI summary
A method for information extraction from an image-based asset includes: generating, by an encoder, at least one image patch from the asset; generating, by the encoder, an input embedding for the at least one image patch; generating, by the encoder, an output embedding based on the input embedding; inferring, by a decoder, a detail of the image-based asset based on the output embedding and a formatted asset, in which the output embedding is sent by the encoder, wherein the formatted asset is sent by a parser; generating, by the decoder, a decoder output based on the detail, in which the detail comprises at least a feature and a second feature; converting, by a converter, the decoder output into an output asset, in which the decoder output is sent by the decoder; and sending, by the converter, the output asset to a user using a graphical user interface (GUI).