Signature Mark Detection in Document Conversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated methods for converting hardcopy documents to digital format face challenges in detecting signature marks, which interrupt the document flow and are often misrecognized by OCR engines due to their small size and irregular occurrence, leading to errors in document ordering and binding.
Innovation Solution
An automated system and method that selects candidate text objects, identifies sequences with incremental and optional fixed numbering patterns, generates models based on detected sequences, and validates these sequences to correctly identify and label signature marks, allowing for missing elements and noise correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If OCR processing is used to automatically identify text elements, then document conversion efficiency is improved, but signature marks are misrecognized or missed due to their small size and irregular occurrence
Solution Approach 1:
The patent segments the document processing into multiple specialized stages: initial OCR processing, candidate signature mark identification based on spatial characteristics (margins, isolation), sequence pattern detection, and validation. This segmentation allows each stage to optimize for its specific function, improving overall accuracy while maintaining efficiency.
Solution Approach 2:
The patent introduces an intermediary validation stage that acts as a mediator between OCR output and final document structure. This validation stage uses sequence pattern analysis and spatial characteristic checking to filter and confirm signature marks, correcting OCR errors without requiring manual review of every element.
2Measurement precision
If manual annotation is used to identify signature marks, then recognition accuracy is improved, but processing time increases significantly
Solution Approach 1:
The system performs self-validation by automatically detecting sequence patterns and spatial characteristics of candidate signature marks. The validation stage uses the detected sequences and models to autonomously confirm or reject candidate marks without human intervention, achieving high accuracy while maintaining automated processing speed.
Solution Approach 2:
The patent implements feedback mechanisms where detected signature mark sequences inform the validation process. The system uses the identified sequences to create models that guide subsequent detection and validation, continuously improving accuracy through iterative refinement without requiring manual correction.
3Ease of operation
If signature marks are removed from converted documents, then document flow is improved, but information about proper binding order is lost
Solution Approach 1:
The patent extracts signature mark information as a separate metadata layer from the main document content. The detected signature marks and their sequences are stored independently, allowing the main document flow to remain clean and readable while preserving the binding order information in an accessible format for reference or processing.
Solution Approach 2:
The patent moves signature mark information from the visual spatial domain (where they appear in margins) to a structured data dimension (sequences and models). This dimensional transformation allows the information to be preserved in a machine-readable format that doesn't interfere with the visual document flow, enabling both clean presentation and accurate binding information retention.
Data Source
AI summary
A system and method for detection of signature marks in documents are provided. The method includes selecting candidate text objects in document pages and identifying a sequence of elements therein. The sequence has a numbering pattern including an incremental part and optionally a fixed part. Missing elements between two detected elements of the sequence are permitted. For an identified sequence, a model of the sequence is generated, which includes the numbering pattern of the sequence, an increment, which is computed based on the distance between pages on which consecutive elements of the sequence are identified, a valid sequence having an increment of greater than 1, and a first page, which corresponds to a page of the document on which the sequence starts. The sequence is then validated with the model, allowing elements of the sequence in the pages of the document to be identified as signature marks.