Document Area Detection Using Multi-Threshold Line Image Analysis
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Solution Overview
Problem
Existing image reading apparatuses face challenges in accurately detecting document areas when adjacent documents stick together, leading to reduced detection sensitivity and increased processing load due to complications in shadow image detection.
Innovation Solution
The apparatus employs a multi-step detection process involving a reading processing unit, first and second detection processing units to identify line images and rectangular areas, and a fourth detection processing unit to accurately distinguish document areas by setting specific threshold values and reference sizes, thereby improving detection accuracy and reducing processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the threshold value is decreased to increase detection sensitivity of the shadow image, then the detection sensitivity improves, but the detection processing becomes complicated and the processing load increases
Solution Approach 1:
The patent segments the detection process into two distinct stages with different threshold values. First, a first threshold value is used to detect prominent line images and define rectangular areas. Then, within those rectangular areas, a second (lower) threshold value is used to detect additional line images. This segmentation allows the system to achieve high detection sensitivity without applying the low threshold globally, thus avoiding excessive processing complexity.
Solution Approach 2:
The patent applies different threshold values in different spatial regions of the image data. The first threshold value is applied to the entire image to detect major line structures, while the second, lower threshold value is applied only within the rectangular areas defined by the first detection. This local quality approach enables sensitive detection where needed while maintaining simpler processing in other regions.
2Measurement precision
If the threshold value is lowered to detect line images from acquired image data, then the detection sensitivity increases, but the processing load of the apparatus increases
Solution Approach 1:
The patent divides the image processing into two sequential phases with different computational requirements. The first phase uses a higher threshold value that requires less processing power to identify major rectangular areas. The second phase uses a lower threshold value but only within the limited scope of the previously identified rectangular areas. This segmentation reduces the overall processing load compared to applying the low threshold to the entire image.
Solution Approach 2:
The patent applies the computationally intensive low-threshold detection only partially - specifically, only within the rectangular areas identified by the first detection phase. This partial action approach achieves the necessary detection sensitivity for stuck document identification while avoiding the excessive processing load that would result from applying the same detection throughout the entire image.
3Measurement precision
If edge portions of adjacent documents stick together without a gap, then the shadow image detection fails, but the number of detected document areas becomes smaller than the actual number of documents
Solution Approach 1:
The patent changes the detection parameter (threshold value) dynamically based on the detection stage and region. By using a first threshold value for initial rectangular area detection and then a second, lower threshold value for detecting line images within those areas, the system can identify subtle separations between stuck documents that would be invisible at higher threshold levels, thereby preventing loss of document count information.
Solution Approach 2:
The patent performs preliminary detection of rectangular areas using the first threshold value before conducting the more sensitive line image detection with the second threshold value. This preliminary action establishes search regions that focus subsequent detection efforts, enabling the system to detect subtle line images between stuck documents while maintaining computational efficiency.
Data Source
AI summary
An image reading apparatus includes a reading processing unit that acquires image data including an image of a document placed on a document placement unit. A first detection processing unit detects a first line image whose width or density exceeds a predetermined first threshold value from the image data. A second detection processing unit detects a rectangular area that is defined by the first line image, included in the image data, and satisfies a predetermined detection condition. A third detection processing unit detects a second line image whose width or density exceeds a second threshold value smaller than the first threshold value from the rectangular area. A fourth detection processing unit detects, as document areas of the respective documents, the rectangular area not divided by the second line image and divided areas included in the rectangular area divided by the second line image.