Error Block Identification for OCR Text Extraction
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Solution Overview
Problem
Existing OCR systems struggle to accurately extract error messages from desktop screen images due to capturing irrelevant text from backgrounds and graphical features, leading to nonsensical recognized text.
Innovation Solution
The system identifies error blocks in images by analyzing properties such as window borders, title bars, saturation, number of widgets, and size, using edge and title bar identifiers, and a scoring system to determine if a window is an error block, thereby isolating the relevant text for OCR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If OCR is performed on the entire desktop screen image, then all text is captured, but irrelevant text from backgrounds and graphical features is included leading to nonsensical recognized text
Solution Approach 1:
The desktop screen image is segmented into multiple regions, and only the error block region is selected for OCR processing. The system identifies the error block by detecting its distinctive visual characteristics (colored title bar, border, widget arrangement) and extracts text only from this specific region, thereby avoiding irrelevant text from other parts of the screen.
Solution Approach 2:
Different regions of the screen are treated differently: the error block region is selected for text extraction while other regions are ignored. The system applies quality filtering by evaluating local visual properties (color saturation of title bar, presence of border, widget density) to determine which region contains the error message worthy of OCR processing.
2Measurement precision
If the system analyzes multiple properties of windows (borders, title bars, saturation, widgets, size), then error block identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary analysis of window properties (detecting borders, title bars, calculating saturation, counting widgets, measuring size) before committing to OCR processing. By evaluating these properties in advance, the system can confidently identify error blocks and avoid unnecessary OCR on non-error windows, thereby justifying the additional processing steps through improved accuracy.
Solution Approach 2:
The system uses a scoring mechanism that aggregates multiple window properties to determine whether a window is an error block. The feedback from property analysis (border presence, title bar saturation, widget count, size metrics) feeds into the identification decision, allowing the system to dynamically adjust its selection based on the combined evidence from all properties.
Data Source
AI summary
In some examples, a window in an image may be identified. At least one property of the window may be identified. Based on the at least one property, whether the window is an error block may be determined.