This is a text spotting model that simultaneously detects and recognizes text. The model detects symbol sequences separated by space and performs recognition without a dictionary. The model is built on top of the Mask-RCNN framework with additional attention-based text recognition head.
Symbols set is alphanumeric: 0123456789abcdefghijklmnopqrstuvwxyz.
This model is a fully-convolutional encoder of text recognition head.
| Metric | Value |
|---|---|
| Word spotting hmean ICDAR2015, without a dictionary | 59.04% |
| GFlops | 2.082 |
| MParams | 1.328 |
| Source framework | PyTorch* |
Hmean Word spotting is defined and measured according to the Incidental Scene Text (ICDAR2015) challenge.
Name: input , shape: [1x64x28x28]. Text recognition features obtained from detection part.
Name: output, shape: [1x256x28x28]. Encoded text recognition features.
[*] Other names and brands may be claimed as the property of others.