Image-to-Document Conversion via Layout Segmentation
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Solution Overview
Problem
Existing OCR technologies struggle to recognize and convert typesetting, table content, and image content in images simultaneously, leading to disordered document formats and high resource consumption for users, resulting in low document recognition efficiency.
Innovation Solution
The method involves performing layout segmentation on images to identify different content types, such as text, tables, and pictures, and processing each type accordingly to improve recognition accuracy, using a combination of OpenCV and deep learning algorithms for edge detection and rectifying distorted images to enhance OCR processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If layout segmentation is not performed on images, then the conversion process is simpler, but typesetting, table content, and image content cannot be recognized in combination with text, causing disordered document formats and low recognition efficiency
Solution Approach 1:
The patent applies layout segmentation to divide the image into multiple layout regions with different content types (text, tables, images, etc.). This segmentation enables the system to recognize and process different content types separately, improving recognition accuracy while maintaining organized document formats.
2Productivity
If layout segmentation and content-type-specific processing are implemented, then document recognition efficiency is improved and user intervention is reduced, but the processing algorithm becomes more complex
Solution Approach 1:
The patent implements content-type-specific processing where different processing algorithms are applied to different layout regions based on their content type. Text regions undergo OCR processing, table regions undergo table structure recognition, and image regions are handled differently. This local quality approach improves overall processing efficiency by optimizing each region's treatment while maintaining manageable algorithm complexity through modular design.
3Ease of operation
If OCR only recognizes text sequentially, then the processing is straightforward, but typesetting and document formatting information is lost requiring extensive user reorganization
Solution Approach 1:
The patent performs layout segmentation and content type identification before OCR text recognition. By preliminarily analyzing the document structure and identifying different content regions, the system preserves typesetting and formatting information in advance. This preliminary action eliminates the need for extensive user reorganization later, as the document structure is maintained throughout the conversion process.
Data Source
AI summary
A method of image-to-document conversion based on optical character recognition (OCR) includes obtaining an image to be converted into a target document, and performing layout segmentation on the image according to image content of the image, to obtain n image layouts, each of the n image layouts corresponding to a content type, and n being a positive integer. The method also includes, for each of the n image layouts, processing image content in the respective image layout according to the content type corresponding to the respective image layout, to obtain converted content corresponding to the respective image layout. The method further includes adding the converted content corresponding to the n image layouts to an electronic document, to obtain the target document.