Abstract: Slab number is a critical identifier in steel manufacturing for process tracking and intelligent logistics. However, poor print quality and harsh industrial environments often damage image data, seriously reducing the accuracy of slab number recognition. To improve the recognition capability of damaged images, a novel algorithm—Feature Recognition Inference Network(FRI-Net)—which combines damaged region detection, contextual feature reasoning, and attention mechanism to achieve high-quality restoration and accurate recognition of degraded slab number images. FRI-Net adopts a modular architecture, introduces a feature feedback optimization mechanism and Knowledge Consistent Attention(KCA), and significantly enhances the restoration capability for complex defective regions. Experimental results on multiple public and industrial datasets demonstrate that FRI-Net outperforms existing mainstream methods in recognition accuracy and fault tolerance, effectively enhancing the stability and intelligent level of slab tracking systems.