Automatic Document Page Indexing via Region-Based Text Extraction
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Solution Overview
Problem
The manual process of adding descriptive metadata to multiple pages in documents, such as project planning drawings, is time-consuming and error-prone, requiring human intervention to extract and label header information for organization and searching purposes.
Innovation Solution
A method for automatically generating unique document page identifiers by selecting regions within pages, extracting text content using OCR if necessary, and assigning it to page location indices without user intervention, enabling automatic bookmarking and labeling across multiple pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual process is used to add descriptive metadata to multiple pages, then accuracy of metadata extraction can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The system automatically extracts text from selected regions on multiple pages and assigns it to page location indices without requiring manual user intervention for each page. The automation processes pages independently using the same region selection, enabling self-service metadata generation across the entire document.
Solution Approach 2:
The text extraction and assignment process is replicated automatically across multiple pages. Once a region is selected on a reference page, the same extraction logic is copied and applied to corresponding regions on all other pages, generating consistent metadata throughout the document without manual repetition.
2Productivity
If automated text extraction is implemented across multiple pages, then productivity and time efficiency improve, but complexity of the processing system increases
Solution Approach 1:
The automation process is divided into distinct segments: region selection on a reference page, text extraction from the selected region, and assignment to page location indices. Each segment is handled independently and sequentially, simplifying the overall system architecture while maintaining high productivity.
Solution Approach 2:
The text extraction and assignment mechanism is designed as a universal process that can be applied to any page in the document using the same region selection. This multi-functional approach allows a single automated process to handle metadata generation across all pages, reducing system complexity compared to page-specific manual processes.
3Reliability
If manual extraction of header information is performed, then control over metadata accuracy is maintained, but the process becomes error-prone and time-consuming
Solution Approach 1:
The system provides feedback by displaying the extracted text to the user before final assignment, allowing verification of accuracy. This feedback mechanism maintains reliability by enabling user review while the automated process handles the time-consuming extraction and assignment tasks across multiple pages.
Data Source
AI summary
Generating unique document identifiers from content within a selected page region is disclosed. A selection of a first region within a first page of the documents is received from a user, and is defined by a set of first boundaries relative to the first page. A text string of a first base selection page content within the first region is retrieved from the first page. Then the retrieved text string is assigned to a page location index associated with the first page. A text string of a first replicated selection page content is retrieved from a second page. The first replicated selection page content is included in the same first region defined by the set of first boundaries relative to the second page. The retrieved text string of the first replicated selection page content is assigned to a page location index of the second page.