论文标题
WSRNET：手写单词的联合发现和识别
WSRNet: Joint Spotting and Recognition of Handwritten Words
论文作者
论文摘要
在这项工作中，我们提出了一个统一的模型，该模型可以通过相同的网络体系结构处理关键字点识别和单词识别。所提出的网络由一个非透明的CTC分支和一个SEQ2SEQ分支组成，该分支通过自动编码模块进一步增强。相关的关节损失会导致识别性能的提升，而SEQ2SEQ分支用于创建有效的单词表示。我们展示了如何通过二进制化进一步处理这些表示形式，以及提供紧凑且高效的描述符的重新培训方案，适用于关键字发现。数值结果验证了所提出的体系结构的实用性，因为我们的方法在关键字点斑点中优于先前的最新方法，并在领先的单词识别方法的标准中提供了结果。
In this work, we present a unified model that can handle both Keyword Spotting and Word Recognition with the same network architecture. The proposed network is comprised of a non-recurrent CTC branch and a Seq2Seq branch that is further augmented with an Autoencoding module. The related joint loss leads to a boost in recognition performance, while the Seq2Seq branch is used to create efficient word representations. We show how to further process these representations with binarization and a retraining scheme to provide compact and highly efficient descriptors, suitable for keyword spotting. Numerical results validate the usefulness of the proposed architecture, as our method outperforms the previous state-of-the-art in keyword spotting, and provides results in the ballpark of the leading methods for word recognition.
