WSRNet: Joint Spotting and Recognition of Handwritten Words

• URL: http://arxiv.org/abs/2008.07109v1
• Date: Mon, 17 Aug 2020 06:22:05 GMT
• Title: WSRNet: Joint Spotting and Recognition of Handwritten Words
• Authors: George Retsinas, Giorgos Sfikas, Petros Maragos
• Abstract summary: The proposed network is comprised of a non-recurrent CTC branch and a Seq2Seq branch that is further augmented with an Autoencoding module. We show how to further process these representations with binarization and a retraining scheme to provide compact and highly efficient descriptors.
• Score: 38.212002652391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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