Glyph Normalization with Discrete Generative Transport for Low Resource Tangut Cursive Script

Tangut cursive script contains extensive stroke connections, omissions, and structural deformations, leading to substantial visual differences from standard printed glyphs and making direct recognition particularly difficult under limited training data. To reduce the influence of complex cursive variations on subsequent recognition and digital processing, we formulate Tangut cursive processing as a generative glyph normalization task, where cursive glyphs are progressively transformed into standard printed representations to provide more stable visual inputs for downstream recognition. Unlike conventional generative models that mainly emphasize visual quality or style transfer, our task places greater emphasis on preserving character structure and identity during transformation. We therefore propose a Source Conditioned Generative Transport Model (SC-GTM) based on discrete latent representations. Cursive and printed glyphs are encoded into a shared visual token space, in which the model progressively updates local probability states through multi-to-multi spatial transport and local token rewriting. The transport process reorganizes observable local information, while the rewriting process accounts for morphological changes and structures that may be partially omitted in cursive writing, without requiring predefined one-to-one correspondences between strokes, components, or tokens. To constrain the underdetermined intermediate transformation, we further introduce path relative-entropy regularization inspired by the Schr\"odinger bridge formulation together with paired endpoint supervision. Experiments on both unseen characters and unseen cursive instances show that SC-GTM achieves competitive visual reconstruction quality and favorable structural consistency, particularly in foreground overlap and skeleton-based measures. Although recognition accuracy is not directly used as an evaluation metric in this work, the results suggest that explicitly modeling spatial reorganization and local rewriting can effectively reduce structural discrepancies between cursive and printed glyphs, providing a promising direction for low-resource Tangut cursive normalization and subsequent automatic recognition.