Feature extractor plays a critical role in text recognition (TR), but
customizing its architecture is relatively less explored due to expensive
manual tweaking. In this work, inspired by the success of neural architecture
search (NAS), we propose to search for suitable feature extractors. We design a
domain-specific search space by exploring principles for having good feature
extractors. The space includes a 3D-structured space for the spatial model and
a transformed-based space for the sequential model. As the space is huge and
complexly structured, no existing NAS algorithms can be applied. We propose a
two-stage algorithm to effectively search in the space. In the first stage, we
cut the space into several blocks and progressively train each block with the
help of an auxiliary head. We introduce the latency constraint into the second
stage and search sub-network from the trained supernet via natural gradient
descent. In experiments, a series of ablation studies are performed to better
understand the designed space, search algorithm, and searched architectures. We
also compare the proposed method with various state-of-the-art ones on both
hand-written and scene TR tasks. Extensive results show that our approach can
achieve better recognition performance with less latency.

本文提出一种利用神经架构搜索算法搜索适合特征提取的方法，设计一个特定领域中的搜索空间，包括针对空间模型和序列模型的 3D 结构空间和变换空间，并引入延迟限制的搜索算法来加速搜索，实验结果表明该方法能够在手写和场景文本识别任务中取得较好的识别性能和更低的延迟。