To extend the proposed detection-based approach for handling more complex document layouts, such as multi-column text or the presence of non-textual elements (e.g., images, tables, or figures), several strategies could be implemented:
Hierarchical Detection Framework: Implement a hierarchical detection framework that first identifies the overall structure of the document, including columns, sections, and non-textual elements. This could involve using a combination of object detection techniques to segment the document into its constituent parts before applying the text line recognition model.
Multi-Scale Feature Extraction: Utilize multi-scale feature extraction techniques to capture text at various resolutions and orientations. This would allow the model to better handle text that is not aligned in a single direction, which is common in multi-column layouts.
Contextual Information Integration: Incorporate contextual information from surrounding text or non-textual elements. For instance, using attention mechanisms to focus on relevant sections of the document could help the model understand the relationship between text and images, improving recognition accuracy.
Adaptive Masking Strategies: Develop adaptive masking strategies that can dynamically adjust based on the layout of the document. For example, if a non-textual element is detected, the model could mask out that area to prevent interference with text recognition.
Training on Diverse Layouts: Augment the training dataset with synthetic examples that mimic complex layouts, including multi-column formats and various non-textual elements. This would help the model generalize better to real-world documents with similar complexities.
By implementing these strategies, the detection-based approach could become more robust and versatile, enabling it to effectively handle a wider range of document layouts and improve overall text recognition performance.