Document Image Orientation Recognition via Multi-Scale Text Line Filtering
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Solution Overview
Problem
Conventional methods for recognizing the orientation of document images are unsatisfactory when the images are a mixture of text and images, as they fail to achieve high accuracy in processing such mixed-content documents.
Innovation Solution
The method involves generating first and second scale binary images, detecting text lines, calculating similarity between corresponding sections, removing noise text lines, and performing optical character recognition to determine the image orientation based on character orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional orientation recognition methods are used on mixed-content document images, then processing can be performed, but recognition accuracy deteriorates due to failure to distinguish text from images
Solution Approach 1:
The patent segments the document image into multiple text line candidates and evaluates each independently using OCR confidence factors. This segmentation allows the system to process text regions separately from image regions, improving orientation recognition accuracy by focusing OCR analysis only on actual text lines rather than treating the entire mixed-content image uniformly.
Solution Approach 2:
The patent applies local quality assessment by evaluating OCR confidence factors for each detected text line candidate individually. Regions with high OCR confidence are identified as genuine text, while regions with low confidence are treated as images or noise. This local differentiation enables accurate orientation recognition in mixed-content documents by applying text-specific analysis only where text is present.
2Measurement precision
If multiple text line candidates are detected and evaluated using OCR for each candidate, then orientation recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements partial action by performing OCR evaluation on multiple text line candidates but using confidence factor thresholds to prune the search space. Instead of exhaustively processing all possible orientations for all candidates, the system performs OCR on candidates and uses confidence scores to eliminate unlikely options, thereby reducing processing time while maintaining high accuracy through selective evaluation.
Solution Approach 2:
The patent employs feedback mechanisms where OCR confidence factors from initial evaluations are used to guide subsequent processing decisions. High-confidence text line detections reinforce the current orientation hypothesis, while low-confidence detections trigger re-evaluation or elimination. This feedback loop enables accurate orientation determination without requiring exhaustive processing of all candidates.
3Measurement precision
If text line detection is performed on full-resolution images, then detection accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent segments the full-resolution document image into multiple text line candidate regions before performing detailed OCR analysis. This preliminary segmentation at reduced resolution or through rapid detection algorithms identifies potential text regions, allowing subsequent high-accuracy OCR processing to be applied only to these segmented candidates rather than the entire image, thereby maintaining detection accuracy while improving processing speed.
Data Source
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AI summary
An image processing method and apparatus is provided. The image processing method includes steps of: generating a first scale binary image from an image, wherein the first scale is smaller than the original scale of the image; detecting at least one text line in the image based on the first scale binary image; generating a second scale binary image from the image, wherein the second scale is larger than the first scale; for each text line, calculating a similarity between corresponding sections in the first scale binary image and the second scale binary image, and removing the text line for which the similarity is lower than a predetermined level; for one or more of the remaining text line(s), performing OCR on corresponding section(s) in the second scale binary image to determine character orientation(s) of corresponding text line(s); and determining the orientation of the image according to the determined character orientation(s).