AI Printed Order Recognition With Template Matching
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Solution Overview
Problem
The manual transcription of orders from paper menus in dining and retail establishments is time-consuming, labor-intensive, and prone to errors, leading to incorrect entries and customer complaints.
Innovation Solution
An AI-powered system that uses image processing and deep learning algorithms to automatically recognize and analyze marked checkboxes and handwritten orders on paper menus, allowing for accurate identification of selected products and quantities, with a guided user confirmation process for review and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual transcription of orders from paper menus is used, then staff can review and verify orders, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service order capture by automatically reading marked menus through image processing and template matching algorithms, eliminating the need for staff to manually transcribe orders while maintaining accuracy through automated detection of selected items and quantities
2Reliability
If manual transcription of orders is used, then staff can verify and correct errors, but the process is prone to human error and time-consuming
Solution Approach 1:
The patent replaces the mechanical manual transcription process with an automated image processing system that uses template matching algorithms to automatically identify selected items and quantities on marked menus, eliminating human error while reducing processing time
Solution Approach 2:
The system provides feedback by displaying the recognized order information on a user interface, allowing staff to review and correct any errors before finalizing the order entry, thus maintaining high accuracy while reducing the time needed for manual transcription
3Productivity
If automated AI-based order recognition is implemented, then order processing speed increases, but the system complexity increases
Solution Approach 1:
The system segments the order recognition process into distinct functional modules: image capture, template matching, indicator field detection, and order data extraction. This segmentation allows each module to be optimized independently and simplifies the overall system architecture while maintaining high processing speed
Solution Approach 2:
The patent introduces templates as intermediary elements that bridge the gap between the physical marked menu and the digital order system. Templates serve as intermediaries that contain pre-defined item names, locations, and indicator field mappings, simplifying the complexity of automated recognition by providing a structured reference framework
4Productivity
If automated AI-based order recognition is implemented, then labor requirements decrease, but technology dependency increases
Solution Approach 1:
The system achieves self-service automation by automatically capturing, processing, and transcribing order information from marked menus without requiring staff intervention in the core order entry process, thereby reducing labor requirements while maintaining high automation level
Solution Approach 2:
The patent creates a universal order recognition system that can handle various menu formats, item types, and marking methods through a single template-based framework, enabling high automation across different dining scenarios while reducing the need for specialized manual processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates fast and accurate capture of ordering information, reducing human error and increasing efficiency by automating the order entry process while enabling staff to verify and modify orders before database transmission.
Implementation Method 1
obtaining, using an image sensor, an image of a first page of an ordering form
Implementation Method 2
recognizing a handwritten number filled in the indicator field using optical character recognition (OCR)
Data Source
AI summary
The present disclosure relates to order recognition methods and systems using artificial intelligence (AI) technologies. In one example, a method includes obtaining, using an image sensor, an image of a first page of an ordering form. The ordering form includes one or more pages, each page including names of items and indicator fields. At least one indicator field of the first page is marked to indicate a corresponding item being ordered. The method further includes determining an order based on the image of the first page and one or more templates associated with the one or more pages of the ordering form. The order includes one or more items selected on the first page of the ordering form. Each template includes items and locations of indicator fields associated with a corresponding page of the ordering form. The method further includes transmitting the determined order to an order processing system.