Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381304080> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4381304080 endingPage "513" @default.
- W4381304080 startingPage "503" @default.
- W4381304080 abstract "With the invention of deep learning, optical CR (OCR) has achieved state-of-the-art performance for Latin, Arabic and Chinese scripts. However for regional languages, lack of large and high-quality training data needed to train the deep neural networks is a major barrier for the usage of deep learning. To produce a diverse synthetic data similar to realistic data, a class of deep networks called Generative Adversarial Networks (GANs) were proposed. GANs have performed efficiently for non Indic scripts and in various other fields like object detection, image translation, etc. Gurumukhi script is one of the regional Indic scripts widely used in North India and some other parts of the world like Canada, USA, etc. In this work, we have evaluated the power of GANs for generating the character dataset of regional language of India. Experimental work has been done using variants of GANs to generate the synthetic data for Gurumukhi script. Deep convolution GAN (DCGAN), Wasserstein GAN (WGAN), Least square GAN (LSGAN) and Conditional GAN (CGAN) have been evaluated for the Gurumukhi handwritten characters dataset. GANs with different network structures, training methods, loss functions and optimizers are used and compared for Gurumukhi handwritten character dataset to find the optimal parameters and results." @default.
- W4381304080 created "2023-06-21" @default.
- W4381304080 creator A5029264462 @default.
- W4381304080 creator A5036970136 @default.
- W4381304080 creator A5052317739 @default.
- W4381304080 date "2023-01-01" @default.
- W4381304080 modified "2023-10-03" @default.
- W4381304080 title "Evaluating Generative Adversarial Networks for Gurumukhi Handwritten Character Recognition (CR)" @default.
- W4381304080 cites W2593414223 @default.
- W4381304080 cites W2922481226 @default.
- W4381304080 cites W2945731760 @default.
- W4381304080 cites W2950534334 @default.
- W4381304080 cites W2955470197 @default.
- W4381304080 cites W2989526993 @default.
- W4381304080 cites W3003221725 @default.
- W4381304080 cites W3003360765 @default.
- W4381304080 cites W3003456222 @default.
- W4381304080 cites W3003747110 @default.
- W4381304080 cites W3003967978 @default.
- W4381304080 cites W3004282887 @default.
- W4381304080 cites W3007878494 @default.
- W4381304080 cites W3026391419 @default.
- W4381304080 cites W3029816221 @default.
- W4381304080 cites W3048939905 @default.
- W4381304080 cites W3055227258 @default.
- W4381304080 cites W3087792628 @default.
- W4381304080 cites W3134812768 @default.
- W4381304080 doi "https://doi.org/10.1007/978-981-99-0085-5_41" @default.
- W4381304080 hasPublicationYear "2023" @default.
- W4381304080 type Work @default.
- W4381304080 citedByCount "0" @default.
- W4381304080 crossrefType "book-chapter" @default.
- W4381304080 hasAuthorship W4381304080A5029264462 @default.
- W4381304080 hasAuthorship W4381304080A5036970136 @default.
- W4381304080 hasAuthorship W4381304080A5052317739 @default.
- W4381304080 hasConcept C108583219 @default.
- W4381304080 hasConcept C111919701 @default.
- W4381304080 hasConcept C115961682 @default.
- W4381304080 hasConcept C153180895 @default.
- W4381304080 hasConcept C154945302 @default.
- W4381304080 hasConcept C204321447 @default.
- W4381304080 hasConcept C2524010 @default.
- W4381304080 hasConcept C2780861071 @default.
- W4381304080 hasConcept C2988773926 @default.
- W4381304080 hasConcept C33923547 @default.
- W4381304080 hasConcept C39890363 @default.
- W4381304080 hasConcept C41008148 @default.
- W4381304080 hasConcept C50644808 @default.
- W4381304080 hasConcept C546480517 @default.
- W4381304080 hasConcept C61423126 @default.
- W4381304080 hasConcept C81363708 @default.
- W4381304080 hasConceptScore W4381304080C108583219 @default.
- W4381304080 hasConceptScore W4381304080C111919701 @default.
- W4381304080 hasConceptScore W4381304080C115961682 @default.
- W4381304080 hasConceptScore W4381304080C153180895 @default.
- W4381304080 hasConceptScore W4381304080C154945302 @default.
- W4381304080 hasConceptScore W4381304080C204321447 @default.
- W4381304080 hasConceptScore W4381304080C2524010 @default.
- W4381304080 hasConceptScore W4381304080C2780861071 @default.
- W4381304080 hasConceptScore W4381304080C2988773926 @default.
- W4381304080 hasConceptScore W4381304080C33923547 @default.
- W4381304080 hasConceptScore W4381304080C39890363 @default.
- W4381304080 hasConceptScore W4381304080C41008148 @default.
- W4381304080 hasConceptScore W4381304080C50644808 @default.
- W4381304080 hasConceptScore W4381304080C546480517 @default.
- W4381304080 hasConceptScore W4381304080C61423126 @default.
- W4381304080 hasConceptScore W4381304080C81363708 @default.
- W4381304080 hasLocation W43813040801 @default.
- W4381304080 hasOpenAccess W4381304080 @default.
- W4381304080 hasPrimaryLocation W43813040801 @default.
- W4381304080 hasRelatedWork W2738221750 @default.
- W4381304080 hasRelatedWork W2763109982 @default.
- W4381304080 hasRelatedWork W2998996837 @default.
- W4381304080 hasRelatedWork W3156786002 @default.
- W4381304080 hasRelatedWork W4200633480 @default.
- W4381304080 hasRelatedWork W4211070796 @default.
- W4381304080 hasRelatedWork W4301431435 @default.
- W4381304080 hasRelatedWork W4321369474 @default.
- W4381304080 hasRelatedWork W564581980 @default.
- W4381304080 hasRelatedWork W4226271949 @default.
- W4381304080 isParatext "false" @default.
- W4381304080 isRetracted "false" @default.
- W4381304080 workType "book-chapter" @default.