Document Structure Conversion via Bitmap Analysis
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Solution Overview
Problem
Conventional OCR systems struggle to effectively convert and maintain the structure and content of documents across different formats, leading to loss of document structure and universality, as they rely on specific format knowledge and are not adaptable to format changes.
Innovation Solution
A system that infers and converts document content and structure from an original format to a target format by analyzing the visual representation, employing heuristics and machine learning to recognize and recreate the original document's functionality, allowing conversion to multiple formats and maintaining format-specific functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCR systems are used to convert documents, then text recognition capability is provided, but document structure and formatting information is lost
Solution Approach 1:
The system segments the document conversion process into distinct components: bitmap analysis for structural element identification, OCR for text recognition, and separate structure recovery mechanisms. This segmentation allows each component to specialize in its strength while the integration preserves both text and structure information.
Solution Approach 2:
The patent introduces an intermediary structure analysis layer that operates between the bitmap and the final document output. This intermediary analyzes the bitmap to identify structural elements (headers, paragraphs, lists, tables) and uses this information to guide the OCR and reconstruction processes, ensuring structure preservation without compromising text recognition accuracy.
2Reliability
If conventional OCR systems parse the original document format, then format-specific conversion is achieved, but universality and adaptability to format changes are lost
Solution Approach 1:
The system employs a universal bitmap-based analysis approach that can handle any document format without requiring format-specific parsers. By converting the document to bitmap and analyzing visual structural characteristics, the system achieves multi-format compatibility while maintaining conversion accuracy through sophisticated structure recognition algorithms.
Solution Approach 2:
Instead of parsing and interpreting original format commands, the system creates a visual copy (bitmap) of the document and analyzes this copy to infer structure. This copying approach allows the system to work with any format that can be rendered as a bitmap, providing universality while maintaining reliability through visual fidelity to the original document.
3Adaptability or versatility
If document structure is recovered from bitmap, then universality across formats is achieved, but complexity of structure inference increases
Solution Approach 1:
The system employs self-service mechanisms where the structure analysis component automatically identifies structural elements by analyzing visual patterns in the bitmap without requiring external format specifications or manual intervention. The system serves itself by using the visual information already present in the bitmap to infer structure, reducing complexity despite the universal approach.
Solution Approach 2:
The patent changes the analysis parameters from format-specific syntax rules to visual structural parameters that can be extracted from any bitmap. By transitioning to parameters such as spatial relationships, element hierarchy, and visual patterns, the system achieves format independence while managing complexity through consistent parameter sets applicable to all document types.
4Measurement precision
If heuristics are used to infer authoring information, then conversion accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial action by using heuristics selectively for specific structural elements where visual cues are most reliable, rather than applying exhaustive analysis to all document elements. This approach achieves high accuracy for critical structural inference while limiting processing time by focusing computational resources on the most informative areas of the document.
Data Source
AI summary
A system that can convert content and structure of a document from an original format into a target format irrespective of the functional specifics of the original format. The system can automatically infer the content and structure of a document via a rendered format thereby restoring the programmatic functionality of the original file (or generating programmatic functionality of a desired target format) through the novel conversion/import process. The system can extract the document structure (e.g., layout) together with the content in order to effectuate the conversion. Heuristics (e.g., logic and/or reasoning) can be employed to make decisions with respect to importing the document into a target format and/or formats.