Inactive evaluations
method: e2e-mlt + bug fix2019-04-11
Authors: Yash Patel, Michal Busta, Jiri Matas
Description: E2E-MLT, an end-to-end trainable unconstrained method for multi-language
scene text localization and recognition. The method is based on a single fully convolutional network (with shared layers for both tasks.
The results are plain network outputs - no dictionary has been used.
method: DIY FOTS2019-04-28
method: TextProposals + DictNet2016-03-02
Authors: Lluis Gomez-Bigorda, Dimosthenis Karatzas
Description: Uses TextProposals [1] (a Text-specific Selective Search Algorithm for Word Spotting in the Wild) in combination with the DictNet [2] CNN.
Source code of the complete end-to-end system is available at: https://github.com/lluisgomez/TextProposals
[1] Lluis Gomez-Bigorda and Dimosthenis Karatzas "TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild", arXiv:1604.02619 2016.
[2] M. Jaderberg, K. Simonyan, A. Vedaldi, A. Zisserman
"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition", Workshop on Deep Learning, NIPS, 2014.
Date | Method | Recall | Precision | Hmean | |||
---|---|---|---|---|---|---|---|
2019-04-11 | e2e-mlt + bug fix | 55.71% | 58.49% | 57.07% | |||
2019-04-28 | DIY FOTS | 54.65% | 58.15% | 56.34% | |||
2016-03-02 | TextProposals + DictNet | 37.89% | 89.84% | 53.30% | |||
2018-10-25 | e2e-mlt | 49.45% | 55.13% | 52.13% | |||
2021-05-25 | Char-level PTSD on SynthText | 18.73% | 33.19% | 23.95% |