Hello,
I'd be glad to help you improve the robustness of your OCR table detection process. Here's a response combining the best aspects of previous suggestions and addressing potential shortcomings:
Understanding Current Limitations:
Search Preference Dependence: Relying solely on searchPref (e.g., spLargest) can be unreliable for complex grids with varying column sizes.
Lack of Visualization: The absence of a preview window makes it difficult to assess the detection area and adjust parameters effectively.
Proposed Enhancements:
Improved Search Area Selection:
Multiple Search Areas: Allow defining multiple search areas within the grid. This enables targeted detection for grids with irregular structures or mixed column sizes.
Visual Selection Tool: Provide a visual tool (e.g., rectangle selection) to define search areas directly on the image. This offers more control and flexibility.
Grid Line Detection and Refinement: Implement algorithms to automatically detect grid lines within the image. Users can then refine these lines or manually adjust the search area based on the detected structure.
Enhanced Preview and Feedback:
Real-time Preview: Implement a real-time preview window that dynamically updates as you adjust search areas and other parameters (e.g., hasHeader). This allows for immediate visual feedback on how changes affect the detection process.
Highlight Detected Areas: Highlight the detected table region, including potential column boundaries, within the preview window. This provides better insight into how the OCR engine is interpreting the layout.
Confidence Scores: If possible, consider displaying confidence scores for detected columns. This information can help users identify areas with lower confidence and potentially adjust parameters or search areas for better results.
Addressing Functionality Gaps:
Table Checkpoint Wizard-like Behavior: While a full-fledged wizard might be outside the scope of this discussion, consider implementing a guided workflow for defining search areas. This could involve:
User selects a general grid region (e.g., entire image or a specific portion).
The system automatically detects potential table structures or suggests a default search area.
Users can then refine the search area visually or provide feedback on the detection.
The refined search area is used for table detection.
Addressing Non-Standard Grids:
Adaptive Grid Detection: Explore algorithms that can handle non-standard grids with varying column spacing or missing headers. This could involve machine learning models trained on diverse grid layouts.
User-Guided Refinement: Even with advanced algorithms, user interaction might still be necessary for complex or challenging grids. Allow users to manually adjust column boundaries or correct detected structures.
By incorporating these enhancements, you can significantly improve the robustness.
