  The flourishing blossom of deep learning has witnessed the rapid development
of Chinese character recognition. However, it remains a great challenge that
the characters for testing may have different distributions from those of the
training dataset. Existing methods based on a single-level representation
(character-level, radical-level, or stroke-level) may be either too sensitive
to distribution changes (e.g., induced by blurring, occlusion, and zero-shot
problems) or too tolerant to one-to-many ambiguities. In this paper, we
represent each Chinese character as a stroke tree, which is organized according
to its radical structures, to fully exploit the merits of both radical and
stroke levels in a decent way. We propose a two-stage decomposition framework,
where a Feature-to-Radical Decoder perceives radical structures and radical
regions, and a Radical-to-Stroke Decoder further predicts the stroke sequences
according to the features of radical regions. The generated radical structures
and stroke sequences are encoded as a Radical-Structured Stroke Tree (RSST),
which is fed to a Tree-to-Character Translator based on the proposed Weighted
Edit Distance to match the closest candidate character in the RSST lexicon. Our
extensive experimental results demonstrate that the proposed method outperforms
the state-of-the-art single-level methods by increasing margins as the
distribution difference becomes more severe in the blurring, occlusion, and
zero-shot scenarios, which indeed validates the robustness of the proposed
method.
