Runlength Histograms for Automated Document Image Characterization
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Solution Overview
Problem
Existing image characterization techniques are manually intensive and not adaptable to scanned documents, requiring manual labeling of images for training and struggling with documents scanned upside down or with varying structure.
Innovation Solution
A method and apparatus that generate a representation of an image using runlength histograms, extracting and combining histograms from multiple regions at different scales to create a vector representation of the image, enabling automated image characterization without optical character recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training images is used for image characterization, then classification accuracy can be achieved, but the process becomes manually intensive and time-consuming
Solution Approach 1:
The system performs automatic image characterization by computing runlength histograms directly from image data without requiring manual labeling. The method extracts features autonomously by analyzing run lengths of pixel values in different directions and combining them into histograms, enabling the system to serve itself rather than relying on manual annotation processes
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational approach. Instead of human operators manually labeling images, the system uses algorithmic computation of runlength histograms to automatically extract meaningful features and characterize images, substituting human labor with automated image processing mechanics
2Extent of automation
If traditional image characterization methods are used, then some level of automation is achieved, but they struggle with scanned documents that are upside down or have varying structures
Solution Approach 1:
The method computes runlength histograms in multiple directions (horizontal, vertical, and diagonal) rather than assuming a single fixed orientation. This asymmetric approach to direction sampling allows the system to capture document structures regardless of their orientation in the image, making the automated characterization robust to upside-down or rotated scanned documents
Solution Approach 2:
The runlength histogram representation serves multiple functions simultaneously: it characterizes document structure, detects orientation, and provides features for classification. This universal representation method works across diverse document types and orientations without requiring separate processing pipelines, enhancing both automation and adaptability
Data Source
AI summary
An apparatus and method are provided for generating a representation of an image which may be used in tasks such as classification, clustering, or similarity determination. An image, such as a scanned document, in which the pixel colorant values are quantized into a plurality of colorant quantization levels, is partitioned into regions, optionally at a plurality of different scales. For each region, a runlength histogram is computed, which may be a combination of sub-histograms for each of the colorant quantization levels and optionally each of plural directions. The runlength histograms, optionally normalized, can then be combined to generate a representation of the document image.