Document Image Recognition for Automated List Management
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Solution Overview
Problem
Users face inefficiencies and time-consuming processes when creating or modifying lists, as they must manually input and manage each item, which discourages the use of electronic lists.
Innovation Solution
The system employs image recognition and OCR algorithms to identify items from electronic images of documents, allowing users to create and modify lists by capturing images of paper documents, thereby automating the process of adding or removing items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual input methods are used to create and modify list items, then list creation is possible, but user input time and operational complexity increase significantly
Solution Approach 1:
The system captures an image of a paper document containing list items and uses OCR technology to automatically extract and populate digital list items, replacing manual typing with automated document copying and recognition
Solution Approach 2:
The patent replaces the mechanical manual typing process with automated optical character recognition and image processing systems, allowing users to simply photograph documents rather than manually input each character
2Ease of operation
If manual methods are used to remove or modify list items, then list modification is possible, but the process becomes cumbersome and time-consuming
Solution Approach 1:
Users capture an image of a modified paper document (such as a receipt or updated list) and the system automatically extracts the updated item information, replacing manual deletion or editing operations with automated document re-capture and comparison
Solution Approach 2:
The system automatically compares the captured document image with the existing digital list, identifies matching items, and performs updates without requiring users to manually navigate through list items or perform multiple editing steps
3Productivity
If extensive manual input is required for list management, then precise list control is achieved, but user effort and time investment increase
Solution Approach 1:
The system replaces manual list management operations with automated optical character recognition, image processing, and text extraction algorithms that can process entire documents simultaneously rather than requiring item-by-item manual input
Solution Approach 2:
The system handles multiple list management functions (creation, modification, deletion, verification) through a single unified process of document capture and OCR processing, allowing one operation to accomplish what previously required multiple separate manual tasks
Data Source
AI summary
A computing device receives a request to render a listing of item entries on a graphical user interface. The computing device receives an electronic image of the document, analyzes the electronic image, and determines a document type by performing an image recognition on a first portion the electronic image, comparing information extrapolated via the image recognition algorithm to a database of document types, and identifying a match between the extrapolated information a document type. The computing device applies an OCR algorithm that corresponds to the determined document type to a second portion of the electronic image, identifies items extracted from the second portion, determines that at least one identified item matches an original item entry, and marks each matching item. The computing device renders an updated listing of item entries on the graphical user interface with a listing of each non-matching item and a marked listing of each matching item.