Barcode ROI Extraction From Mailpiece Label Images
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Solution Overview
Problem
Existing image processing systems for extracting regions of interest, such as barcodes from captured images of items like mailpieces or parcel labels, are inefficient and resource-intensive, particularly due to the need for time-consuming optical character recognition (OCR) processes.
Innovation Solution
A system and method for identifying and extracting a region of interest (ROI) from a captured image by using a processor to generate binary image data, identify the ROI, and remove unnecessary pixel regions, employing filters and edge detection to isolate the barcode, and using image masks to enhance the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing systems process the entire captured image to extract barcodes, then comprehensive image analysis is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the captured image into multiple regions and processes only the region containing the barcode. The system divides the image into a first region (containing the barcode) and a second region (other areas), then applies image processing operations only to the first region, thereby reducing processing time while maintaining extraction accuracy.
Solution Approach 2:
The patent extracts the barcode region from the entire captured image by identifying pixel regions corresponding to the barcode and separating them from the rest of the image. This extraction allows the system to focus computational resources only on the relevant portion containing the barcode, significantly reducing processing time and resource consumption.
2Reliability
If the entire captured image is processed to ensure complete barcode detection, then detection reliability is maintained, but computational resources are wasted on irrelevant areas
Solution Approach 1:
The patent applies different processing qualities to different regions of the image. The first region containing the barcode receives full processing attention for reliable detection, while the second region is either processed with reduced quality or excluded entirely. This local quality approach ensures barcode detection reliability is maintained in the critical region while minimizing computational resource consumption overall.
3Loss of information
If optical character recognition processes are applied to the entire image, then all text elements are identified, but processing complexity and time increase
Solution Approach 1:
The patent performs preliminary actions to identify and isolate the barcode region before applying optical character recognition processes. By first segmenting the image and extracting the region containing the barcode, the system prepares the data in advance, allowing OCR to be applied efficiently only to the relevant portion rather than the entire image, thereby reducing processing complexity while maintaining information completeness.
Data Source
AI summary
The present disclosure relates to a system and method for extracting a region of interest from a captured image of an item. The system may include a reader configured to capture an image of an item having a computer readable code positioned thereon. The system may also include a processor in data communication with the reader and configured to generate captured image data, the captured data comprising binary image data and identify a first pixel region representing the computer readable code from the binarized image data. The processor may be further configured to remove a second pixel region other than the first pixel region from the binarized image data and store or process only first binarized image data representing the first pixel region.