Receipt Image Text Extraction via Pixel Saturation Clustering
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Solution Overview
Problem
Current methods for extracting region of interest text from receipts are inefficient and prone to errors due to manual examination and processing, especially when dealing with multiple receipts for high-volume data collection.
Innovation Solution
The system employs image processing techniques such as image segmentation, pixel format conversion, and contour detection to automate the extraction of text from regions of interest in receipt images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual examination and processing methods are used to extract text from receipts, then the process is simple to implement, but the productivity is low and error-prone when dealing with multiple receipts
Solution Approach 1:
The patent replaces manual mechanical examination with an automated image processing system that uses computer vision algorithms to detect, segment, and extract text from receipt images. The system automatically identifies regions of interest, performs optical character recognition, and structures the extracted data without human intervention, thereby dramatically improving productivity while managing complexity through standardized processing pipelines.
Solution Approach 2:
The system enables self-service text extraction by automatically processing receipt images through a series of computational steps including preprocessing, contour detection, text region identification, and OCR. The automated pipeline handles multiple receipts independently, extracting relevant information without requiring manual intervention for each document, thus achieving high throughput and consistency.
2Measurement precision
If automated image processing techniques are employed to extract text from receipts, then the productivity and accuracy are improved, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the receipt image processing into distinct stages: preprocessing (noise reduction, contrast enhancement), contour detection (identifying text boundaries), region of interest identification (locating specific data fields), and OCR (text recognition). This segmented approach improves accuracy by optimizing each stage independently while managing overall system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by preprocessing the receipt image before main text extraction operations. Steps include adjusting image quality, normalizing formats, and pre-identifying potential text regions. These preliminary actions improve the accuracy of subsequent extraction steps by ensuring the input data is optimized for processing, thereby reducing errors in the final text recognition.
3Loss of time
If manual processing is used for high-volume receipt data collection, then the system complexity remains low, but the loss of time increases significantly
Solution Approach 1:
The patent implements continuous processing by designing an automated pipeline that handles multiple receipts in sequence without interruption. The system continuously performs preprocessing, contour detection, region identification, and OCR extraction across all input documents, eliminating the stop-start nature of manual processing. This continuous action dramatically reduces total data collection time while maintaining high productivity through efficient resource utilization.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for text extraction from a receipt image. An example non-transitory computer readable medium comprises instructions that, when executed, cause a machine to at least improve region of interest detection efficiency by converting pixels of an input receipt image from a first format to a second format, generate a binary representation of the input receipt image based on the converted pixels, the binary representation of the input receipt image corresponding to saturation values for respective ones of the converted pixels, calculate mirror data from the binary representation of the input receipt image, and cluster the binary representation of the input receipt image to identify a first set of candidate regions of interest, the candidate regions of interest characterized by portions of the binary representation of the input receipt image having saturation values that satisfy a threshold value.