Fetching the paper…
Reading the bibliography…
Text image super-resolution is a challenging yet open research problem in the computer vision community.
Moghaddam, R.F., Cheriet, M.: A multi-scale framework for adaptive binarization of degraded document images. Pattern Recognition 43(6), 2186–2198 (2010)
2010
Earlier work this paper cites.
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and Surfaces, pp. 711–730 (2012)
2012
Earlier work this paper cites.
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184–199 (2014)
2014
Earlier work this paper cites.
Timofte, R., De Smet, V., Van Gool, L.: A+: Adjusted anchored neighborhood regression for fast super-resolution. In: IEEE Asian Conference on Computer Vision (2014)
2014
Earlier work this paper cites.
Cl¨¦ment Peyrard, Franck Mamalet, C.G.: a comparison between multi-layer perceptrons and convolutional neural networks for text image super-resolution. In: VISAPP (2015)
2015
Cited alongside, same era.
Cl¨¦ment Peyrard, Moez Baccouche, F.M.C.G.: ICDAR2015 competition on text image super-resolution. In: International Conference on Document Analysis and Recognition (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Tesseract-OCR. http://code.google.com/p/tesseract-ocr/
Cited in the paper.
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)
2015
Closest in time.
Ouyang, W., Luo, P., Zeng, X., Qiu, S., Tian, Y., Li, H., Yang, S., Wang, Z., Xiong, Y., Qian, C., et al.: Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection. IEEE Conference on Computer Vision and Pattern Recognition (2015)
2015
Closest in time.
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection. International Journal on Document Analysis and Recognition (IJDAR) pp. 1–21 (2015)
2015
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…