Form Document Content Extraction via Background Masking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current OCR/ICR technologies face challenges in accurately processing documents with warped or distorted images, mixed typed and handwritten information, and varying image quality, leading to high failure conversion rates and the need for human intervention.
Innovation Solution
A system and method that prepares documents by removing form elements and distortions, using artificial intelligence and machine learning to mask background data and extract relevant content, allowing for more accurate OCR/ICR processing without requiring character recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard OCR/ICR techniques are applied to scanned documents, then text can be converted to machine-encoded text, but the recognition rate is low when images are warped or distorted
Solution Approach 1:
The system performs preliminary actions by detecting and correcting image distortions, warping, and skewing before applying OCR/ICR techniques. This preprocessing includes identifying the document layout, correcting perspective distortions, and normalizing the image quality to ensure optimal recognition conditions for subsequent text extraction
Solution Approach 2:
The system introduces an intermediary processing layer between the scanned document and OCR/ICR engine. This intermediary layer includes image enhancement, noise reduction, and feature extraction components that prepare the document image by removing artifacts and highlighting text regions, thereby improving the input quality for recognition
2Productivity
If OCR/ICR is applied to documents with mixed typed and handwritten information, then text extraction is attempted, but differentiation between typed and handwritten text becomes difficult
Solution Approach 1:
The system applies local quality analysis by examining different regions of the document independently. It identifies specific characteristics of typed versus handwritten text in different areas and applies appropriate recognition methods tailored to each region's content type, thereby improving overall differentiation accuracy
3Ease of manufacture
If documents are scanned and saved as images for processing, then document storage is simplified, but image quality issues such as scanning artifacts and printing errors occur
Solution Approach 1:
The system extracts and removes harmful elements from scanned images including scanning artifacts, printing errors, noise, and distortions. By isolating and eliminating these detrimental factors while preserving the original text content, the system improves image quality without requiring re-scanning or manual intervention
4Measurement precision
If human data entry is used to manually extract text from difficult-to-recognize documents, then recognition accuracy can be improved, but manual labor and time consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically detecting document types, identifying text regions, and extracting information without requiring human intervention. The automated system serves itself by adapting to different document formats and quality levels, processing documents independently while maintaining high accuracy rates
Data Source
AI summary
The present disclosure generally provides a system and method for processing and identifying data in form. The system and method may distinguish between content data and background data in a form. In some aspects, the content data or background data may be removed, wherein the remaining data may be processed separately. Removal of the background data or the content data may allow for more effective or efficient character recognition of the data. In some embodiments, data may be processed on an element basis, wherein each element of the form may be labeled as background data, content data, noise, or combinations thereof. This system and method may significantly increase the ability to capture and extract relevant information from a form.