Printed Grid Patterns for Smartphone Document Scanning
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Solution Overview
Problem
Smartphone document scanning faces challenges such as obtaining quality images of handwritten pages due to variable lighting, perspective distortion, and background effects, while QR code scanning is aesthetically unappealing and sensitive to location and lighting conditions, leading to unreliable recognition.
Innovation Solution
The system uses pre-printed uniform grid patterns on paper to detect and adjust images, removing distortions and shadows, and employs smart patterns on surfaces to facilitate image processing, including color and brightness correction, and object identification through content-based image retrieval techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If QR codes are used for mobile tagging, then product information can be obtained, but the scanning process is aesthetically unappealing and sensitive to lighting conditions
Solution Approach 1:
The patent segments the identification function from the traditional QR code format and integrates it into the existing grid pattern structure. Instead of using separate QR codes that require specific lighting and positioning, the identification data is embedded within the grid pattern itself, making the scanning process more robust to lighting variations while maintaining aesthetic appeal.
Solution Approach 2:
The patent merges the QR code functionality with the grid pattern background. The grid pattern serves dual purposes: as an aesthetic/design element and as the carrier for identification data. This combination eliminates the need for separate QR code placement and reduces sensitivity to lighting conditions by distributing identification information across the entire pattern rather than concentrating it in a small, vulnerable area.
2Productivity
If traditional document scanning is performed, then photographs can be captured, but perspective distortion and lighting variations degrade image quality
Solution Approach 1:
The patent introduces the grid pattern as an intermediary reference system between the camera and the document. The grid serves as a mediator that provides known geometric relationships and color references, enabling the software to calculate and correct perspective distortion and lighting variations automatically. This intermediary structure allows rapid scanning while maintaining high image quality through computational correction.
Solution Approach 2:
The patent replaces mechanical precision requirements (perfectly perpendicular camera alignment, controlled lighting) with computational methods. Instead of requiring the user to physically position the camera at exact angles or control lighting conditions, the system uses the grid pattern as a reference for software-based perspective correction and color normalization, substituting mechanical precision with algorithmic processing.
3Measurement precision
If page boundaries are manually identified, then document structure can be determined, but the process is time-consuming and error-prone
Solution Approach 1:
The patent performs preliminary action by pre-printing the grid pattern on the document pages before scanning. This pre-established reference structure eliminates the need for real-time boundary detection algorithms to search for and interpret document features during scanning. The grid pattern is already in place to guide the software immediately, providing pre-defined reference points for rapid and accurate boundary identification.
Solution Approach 2:
The patent uses the regular, repeating grid pattern as a template that can be easily copied and recognized across multiple pages. The software can quickly match the expected grid structure against the captured image, rapidly identifying page boundaries by detecting where the grid pattern begins and ends. This template-matching approach is much faster and more accurate than general-purpose boundary detection methods.
Data Source
AI summary
Identifying at least one object in data for photographed images includes detecting a reference pattern in the data, locating the reference pattern in a table of patterns, and identifying the at least one object according to the reference pattern. The pattern may be located on the at least one object. The at least one object may be a greeting card. The pattern may categorize the greeting card by season and/or occasion. The pattern may identify a sender of the greeting card. The pattern may be provided on an item that is separate from the at least one object. The table of patterns may include a record for at least some of the patterns and the record may include additional information associated with the pattern. The additional information may include tasks that are performed in connection with the pattern. The additional information may include geometric and color parameters of the pattern.