| # Custom CTC Loss | |
| CTC Loss but in addition to a classification label per time step we have an additional set of real values as targets (assume Gaussian distributed) per time step and a new BLANK1 symbol for masking real value targets. | |
| # How this is used in OCR? | |
| We use 6 real values to predict color of font and border color per character, we use space character as BLANK1 symbol. | |
| # Acknowledgement | |
| Original code is from https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/LossCTC.cu | |
| # Reference | |
| ``` | |
| [1] Graves, A., Fernández, S., Gomez, F. and Schmidhuber, J., 2006, June. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on Machine learning (pp. 369-376). | |
| [2] Wigington, C., Price, B. and Cohen, S., 2019, September. Multi-label connectionist temporal classification. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 979-986). IEEE. | |
| ``` | |