Layout-Preserving OCR for Document Information Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for extracting information from documents, such as invoices and bank statements, are inefficient due to unstructured Optical Character Recognition (OCR) results and the need for manual correction, especially with varying fonts and layouts, leading to high variance and complex document structures.
Innovation Solution
A system utilizing a layout-preserving OCR module, tagging module, and machine learning modules to identify and classify textual and graphical elements within documents, enabling precise extraction of information through training on a corpus of documents, which includes image pre-processing, geometric correction, and classification using neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional OCR techniques are used to automatically extract text from documents, then extraction speed is improved, but accuracy deteriorates due to unstructured results and high variance in fonts and layouts
Solution Approach 1:
The system segments the document processing into multiple stages: OCR text extraction, layout analysis to identify logical blocks, and machine learning-based classification. This segmentation allows each component to specialize, improving overall accuracy while maintaining speed.
Solution Approach 2:
A machine learning model serves as an intermediary between OCR extraction and final information retrieval. The model receives both extracted text and layout information, then classifies and structures the data, resolving the accuracy-speed contradiction by adding an intelligent processing layer.
2Measurement precision
If manual review is performed to correct OCR errors, then extraction accuracy is improved, but time consumption increases
Solution Approach 1:
The machine learning model performs self-correction by automatically classifying extracted text and identifying logical blocks. The system serves itself by using trained models to correct OCR errors without human intervention, maintaining high accuracy while eliminating manual review time.
Solution Approach 2:
The system uses feedback from layout analysis and classification results to improve OCR extraction. The machine learning model learns from corrected examples and adjusts its classification, creating a feedback loop that reduces manual review needs while maintaining accuracy.
3Ease of operation
If natural language processing is used for information retrieval, then ease of operation is improved, but extraction precision deteriorates due to unstructured scanning environments and complex document structures
Solution Approach 1:
The system adds a layout analysis dimension to traditional NLP processing. By incorporating spatial information and logical block identification alongside text processing, the system maintains operational simplicity while significantly improving extraction precision through multi-dimensional analysis.
Data Source
AI summary
A method of training a system to extract information from documents comprises feeding digital form of training documents to an OCR module, which identifies multiple logical blocks in the documents and text present in the logical blocks. One or more tags for the whole of the document, the logical blocks and word tokens on the document are received by a tagging module. A text input comprising the text identified in the document and the tags for the whole of the document are received by a machine learning module. A first image of the document with layout of the one or more of the identified blocks superimposed, and the tags of the logical blocks in the document are received by the machine learning module, wherein the received text input, first image and tags for the logical blocks corresponds to a plurality of the training documents.