Text Refinement Network for Image Segmentation

The patent applies local quality by introducing a text-specific refinement module that processes only the text portions identified in the initial segmentation. This module uses text-aware features (such as character structure, font properties, and text-specific patterns) to refine the segmentation results specifically for text regions, while leaving non-text regions processed by the general segmentation network unchanged. This resolves the contradiction by enhancing text segmentation accuracy through specialized processing without requiring the entire system to be reconfigured for text-specific properties.

The patent employs a two-stage segmentation approach: first, a general image segmentation network performs initial segmentation to identify potential text regions; second, a text refinement network processes these regions with text-specific features to improve accuracy. This multi-level segmentation strategy allows the system to maintain general image processing capability while adding specialized text awareness where needed, thus resolving the contradiction between general versatility and text-specific precision.

The patent applies preliminary action by first performing a general image segmentation to identify candidate text regions, then using this initial segmentation as input to the text refinement network. The refinement network uses text-specific features to pre-process and highlight likely text portions before final classification. This two-stage approach improves precision by preparing the data in advance with text-aware processing, while keeping the overall system complexity manageable through modular architecture.

The patent introduces an intermediary text refinement network that acts as a bridge between the general image segmentation and final text classification. This intermediary module processes the initial segmentation results through text-specific feature extraction and refinement, improving precision by filtering out false positives (curves and decorations) and enhancing true text regions. The intermediary approach adds only moderate complexity while significantly improving segmentation precision.

The patent applies local quality by directing computational resources specifically to text regions identified in the initial segmentation. The refinement network processes only these candidate text regions using text-specific features, rather than applying heavy processing to the entire image. This localized refinement approach improves classification accuracy for text portions while minimizing additional computational energy consumption by avoiding unnecessary processing of non-text regions.

The patent implements partial action by applying the computationally intensive text refinement process only to regions identified as potential text in the initial segmentation, rather than processing the entire image. This selective application of refinement reduces overall computational energy consumption while still achieving high classification accuracy for text portions. The system performs excessive processing only where needed (in text regions) and minimal processing elsewhere.