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Separating Optical and Language Models through Encoder-Decoder Strategy for Transferable Handwriting Recognition

Adeline Granet 1, 2 Emmanuel Morin 2 Solen Quiniou 2 Christian Viard-Gaudin 1 Harold Mouchère 1
1 IPI - Image Perception Interaction
LS2N - Laboratoire des Sciences du Numérique de Nantes
2 TALN - Traitement Automatique du Langage Naturel
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Lack of data can be an issue when beginning a new study on historical handwritten documents. To deal with this, we propose a deep-learning based recognizer which separates the optical and the language models in order to train them separately using different resources. In this work, we present the optical encoder part of a multilingual transductive transfer learning applied to historical handwriting recognition. The optical encoder transforms the input word image into a non-latent space that depends only on the letter-n-grams: it enables it to be independent of the language. This transformation avoids embedding a language model and operating the transfer learning across languages using the same alphabet. The language decoder creates from a vector of letter-n-grams a word as a sequence of characters. Experiments show that separating optical and language model can be a solution for multilingual transfer learning.
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Submitted on : Friday, June 22, 2018 - 3:41:28 PM
Last modification on : Thursday, February 27, 2020 - 1:16:45 AM
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  • HAL Id : hal-01821598, version 1

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Adeline Granet, Emmanuel Morin, Solen Quiniou, Christian Viard-Gaudin, Harold Mouchère. Separating Optical and Language Models through Encoder-Decoder Strategy for Transferable Handwriting Recognition. 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Aug 2018, Niagara Falls, Canada. ⟨hal-01821598⟩

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