Abstract
Market demand has primarily driven scene text recognition to focus on widely spoken languages, such as English and Chinese. Current research put scarce attention on Tibetan, despite its substantial demand and potential applications. To fill this gap, we analyze the characteristics of Tibetan text and point out the corresponding difficulties in text recognition. Moreover, this study explores the performance degradation of existing recognition methods on Tibetan text, and proposes a flexible alignment decoding manner with cross-modal sequence reasoning for Tibetan text. Concretely, our work leverages the information between different modal sequences to learn detailed visual features for Tibetan characters with complex glyphs through cross-modal sequence reasoning. Furthermore, we design an innovative alignment decoding scheme that aligns characters in a dense manner, improving attention quality for the character alignment. Experimental results indicate that the performance of existing text recognition on Tibetan text is far behind that of English or Chinese text. With our design, the recognition on Tibetan text achieves significant improvement.
Published Version
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