Text spotting for large-angle curved and rotated coupled text in complex industrial scenarios faces persistent challenges. Traditional character-level annotation-based methods struggle to adapt to dynamic deformations, while existing regression-based end-to-end networks exhibit limited modeling capabilities for complex shapes, leading to significant regression errors in large-angle curved scenarios and severely compromised robustness in rotated text detection. To address the geometric modeling difficulties of large-angle curved text, in this paper, we propose a parameterized modeling method based on piecewise cubic Hermite interpolation curves. By leveraging derivative continuity constraints, it generates highly smooth text boundaries and significantly improves the fitting accuracy for curved text. Concurrently, we construct a dynamic feature alignment layer named HermiteAlign by integrating orthogonal sampling grids with bilinear interpolation, effectively alleviating feature distortion. To overcome the robustness bottleneck in detecting large-angle rotated text, we introduce a multi-kernel bounding box fusion mechanism. This approach dynamically selects optimal bounding box through semantic segmentation and four-directional sub-region transformation, combined with semantic completeness evaluation, ensuring rotation-invariant feature representation. Furthermore, to validate the effectiveness of the proposed method in industrial scenarios, we provide a dataset containing abundant instances of large-angle curved and rotated text. Extensive experiments demonstrate that our method achieves state-of-the-art performance, providing an efficient solution for large-angle deformed text spotting in complex industrial environments.