Name
Title | ||
|---|---|---|
Separating Optical and Language Models Through Encoder-Decoder Strategy for Transferable Handwriting Recognition |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICFHR-2018.2018.00061 | 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) |
Keywords | Field | DocType |
Handwriting recognition, knowledge transfer, Optical model, Language model | Transduction (machine learning),Rotary encoder,Encoder decoder,Embedding,Pattern recognition,Computer science,Knowledge transfer,Transfer of learning,Handwriting recognition,Speech recognition,Artificial intelligence,Language model | Conference |
ISSN | ISBN | Citations |
2167-6445 | 978-1-5386-5876-5 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adeline Granet | 1 | 0 | 1.69 |
| Emmanuel Morin | 2 | 42 | 16.13 |
| Harold Mouchère | 3 | 107 | 14.46 |
| Solen Quiniou | 4 | 71 | 9.97 |
| Christian Viard-Gaudin | 5 | 444 | 46.20 |