Imaging Device Detection of Non-Payload Features for Edge OCR
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Solution Overview
Problem
Barcode reading systems require manual switching between barcode scanning and image analysis modes, leading to increased resource usage, distraction, and human error, especially when additional information beyond the barcode needs to be captured.
Innovation Solution
An imaging device that automatically detects non-payload encoding visual features, such as human faces or indicia, and initiates optical character recognition (OCR) operations using edge-computing, without interrupting the workflow and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual switching between barcode scanning and image analysis modes is implemented, then additional information capture capability is improved, but user workflow efficiency deteriorates due to distraction and human error
Solution Approach 1:
The system automatically detects whether to perform barcode decoding or OCR operations without requiring user intervention. The imaging device autonomously determines the appropriate processing mode based on the captured image content, eliminating manual switching and associated distractions while maintaining the ability to capture both barcode and non-barcode information.
2Adaptability or versatility
If manual mode switching is required, then additional information capture is enabled, but resource usage increases due to constant communication and processing
Solution Approach 1:
The system performs preliminary analysis of the captured image to determine whether barcode decoding or OCR processing is appropriate before initiating the corresponding operation. This preliminary detection step prevents unnecessary processing and communication overhead by only activating the appropriate processing pipeline when needed, thereby reducing overall resource consumption while maintaining versatility.
3Ease of operation
If specialized software is used for mode switching assistance, then mode transition capability is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex specialized software with a simplified image analysis approach using standard imaging components and basic processing algorithms. The system uses the imaging device's native capabilities to detect visual features and determine processing mode, eliminating the need for specialized switching software while maintaining ease of operation.
4Reliability
If constant image analysis is performed to assist mode entry, then mode transition accuracy is improved, but power consumption increases drastically
Solution Approach 1:
Instead of continuous image analysis, the system performs image analysis periodically or on-demand based on trigger events such as barcode detection attempts or visual feature recognition. This periodic processing approach maintains accurate mode transition detection while significantly reducing power consumption compared to constant analysis.
Data Source
AI summary
Imaging devices, systems, and methods for determining whether an object is within range to be decoded based on a sharpness of the object in a captured image are described herein. An example device includes: an imaging assembly configured to capture image data of an object appearing in a field of view (FOV); one or more processors; and one or more computer-readable media storing machine readable instructions that, when executed, cause the one or more processors to: (i) capture, using the imaging assembly, the image data of the object appearing in the FOV; (ii) attempt to decode the image data of the object; (iii) responsive to an unsuccessful attempt to decode the image data, detect a non-payload encoding visual feature; and (iv) responsive to detecting the non-payload encoding visual feature, transmit, to an edge-computing module, a request for an optical character recognition (OCR) operation to be performed for the object.