Abstract

Transformer-based Optical Character Recognition (OCR) systems have recently demonstrated strong performance by modeling long-range dependencies in text images. However, most existing approaches rely on single-scale visual representations, which limits their robustness in scenarios involving variable font sizes, degraded characters, and complex document layouts. This study proposes a Multi-Scale Feature-Based Transformer (MSFT-OCR) that explicitly integrates fine-, mid-, and coarse-scale visual features using scale-aware attention mechanisms. The proposed architecture enables effective interaction between character-level details and global word-level context through inter-scale attention. Extensive experiments on scene text and document OCR benchmarks demonstrate that the proposed method consistently outperforms single-scale Transformer models on IIIT5K-Words, IAM, SVT on basis of evaluation metrics CA(%), WA(%), NED(%). Ablation studies and attention visualizations further validate the effectiveness of multi-scale modeling in text recognition.

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