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Generating synthetic images is an art which emulates the natural process of image generation in a closest possible manner.
Word spotting: A new approach to indexing handwriting
Manmatha, R., Han, C., Riseman, E.M.: · 1996
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The IAM-database: an English sentence database for offline handwriting recognition
Marti, U., Bunke, H.: · 2002
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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A similarity measure between vector sequences with application to handwritten word image retrieval
Rodríguez-Serrano, J.A., Perronnin, F., Lladós, J., Sánchez, G.: · 2009
Earlier work this paper cites.
Retrieval of online handwriting by synthesis and matching
Jawahar, C.V., Balasubramanian, A., Meshesha, M., Namboodiri, A.M.: · 2009
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Character recognition in natural images
de Campos, T.E., Babu, B.R., Varma, M.: · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010
Cited alongside, same era.
Nearest neighbor based collection OCR
Sankar, K.P., Jawahar, C.V., Manmatha, R.: · 2010
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Cited alongside, same era.
Lexicon-free handwritten word spotting using character HMMs
Fischer, A., Keller, A., Frinken, V., Bunke, H.: · 2012
Cited alongside, same era.
Building a volunteer community: results and findings from transcribe bentham
Causer, T., Wallace, V.: · 2012
Cited alongside, same era.
End-to-end text recognition with convolutional neural networks
Wang, T., Wu, D.J., Coates, A., Ng, A.Y.: · 2012
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Synthetic data and artificial neural networks for natural scene text recognition
Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
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Image-based synthesis and re-synthesis of viewpoints guided by 3d models
Rematas, K., Ritschel, T., Fritz, M., Tuytelaars, T.: · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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On rendering synthetic images for training an object detector
Rozantsev, A., Lepetit, V., Fua, P.: · 2015
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
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Cited alongside, same era.
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: · 2016
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