AI Menu Content Recognition via OCR and Semantic Analysis
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Solution Overview
Problem
Manual data extraction from documents, such as restaurant menus, is inefficient and labor-intensive, especially when menu information needs to be frequently updated.
Innovation Solution
The use of pattern recognition and AI technology to automatically recognize content in documents, including optical character recognition (OCR) for text extraction, automatic classification of text blocks into styles, determination of content types, and identification of semantic relationships between text blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data extraction from paper menus is used, then data can be entered into the restaurant management system, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of data entry with an automated optical character recognition (OCR) system. The OCR technology automatically extracts text from menu images and converts it into editable digital formats, eliminating the need for manual typing and significantly reducing the time and labor required for data entry operations.
Solution Approach 2:
The system enables self-service by allowing the menu data extraction process to occur automatically without human intervention. The OCR system independently captures menu images, processes the text, classifies content types, and populates the management system database, making the entire workflow autonomous and eliminating dependency on manual labor.
2Adaptability or versatility
If menu information is manually entered into the restaurant management system, then the system can be updated, but the process is labor-intensive and repetitive
Solution Approach 1:
The patent replaces repetitive manual data entry operations with automated OCR technology that captures menu images and automatically extracts, classifies, and structures the information. This substitution maintains the system's adaptability to handle various menu formats while dramatically simplifying the update process and reducing operational complexity.
Solution Approach 2:
The system performs preliminary classification of text blocks into different content types (dish names, descriptions, prices, categories) during the initial OCR processing stage. This preliminary action organizes the extracted data into structured formats before population into the management system, making subsequent updates more efficient and reducing the complexity of ongoing menu maintenance operations.
3Adaptability or versatility
If paper menus are re-designed and printed frequently, then menu adjustments can be made, but menu information must be re-entered into the system each time
Solution Approach 1:
The patent establishes a continuous automated workflow where menu images are continuously captured, processed through OCR, and updated in the management system without interruption. This continuous action eliminates the need for repeated manual data entry whenever menus are updated, maintaining high productivity even as menu flexibility increases and update frequency rises.
Solution Approach 2:
The system creates digital copies of menu information through OCR technology, extracting text from menu images and converting it into editable digital formats. These digital copies can be easily updated and re-populated in the management system without requiring physical re-entry, enabling frequent menu adjustments while maintaining high data entry productivity through automated copy-and-update operations.
Data Source
AI summary
The present disclosure relates to systems, software, and computer-implemented methods that automatically recognize content in a document. An example method includes obtaining an image of the document, where the image includes a plurality of text blocks. The method further includes determining textual information of each text block using optical character recognition (OCR) and automatically classifying the plurality of text blocks into a plurality of styles. The method further includes automatically determining a content type of each text block based on a style associated with the text block and textual information of the text block. The method further includes determining semantic relationships between the plurality of text blocks based on one or more of the content type, the textual information, or a location of each text block.