Deep metric learning plays an important role in measuring similarity through distance metrics among arbitrary group of data. MNIST dataset is typically used to measure similarity however this dataset has few seemingly similar classes, making it less effective for deep metric learning methods. In this paper, we created a new handwritten dataset named Urdu-Characters with set of classes suitable for deep metric learning. With this work, we compare the performance of two state-of-The-Art deep metric learning methods i.e. Siamese and Triplet network. We show that a Triplet network is more powerful than a Siamese network. In addition, we show that the performance of a Triplet or Siamese network can be improved using most powerful underlying Convolutional Neural Network architectures.

Hand written characters recognition via deep metric learning

Nawaz, Shah;Calefati, Alessandro;Gallo, Ignazio
2018-01-01

Abstract

Deep metric learning plays an important role in measuring similarity through distance metrics among arbitrary group of data. MNIST dataset is typically used to measure similarity however this dataset has few seemingly similar classes, making it less effective for deep metric learning methods. In this paper, we created a new handwritten dataset named Urdu-Characters with set of classes suitable for deep metric learning. With this work, we compare the performance of two state-of-The-Art deep metric learning methods i.e. Siamese and Triplet network. We show that a Triplet network is more powerful than a Siamese network. In addition, we show that the performance of a Triplet or Siamese network can be improved using most powerful underlying Convolutional Neural Network architectures.
2018
Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018
978-153863346-5
13th IAPR International Workshop on Document Analysis Systems, DAS 2018
TU WienVienna; Austria
24 April 2018 through 27 April 2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2073515
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