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A pytorch implementation of CRNN，and test it with IIIT-5K.
Support PyTorch 1.0 now.
Netword Struct：
| Type | Configurations | Output Size |
|---|---|---|
| Input | W × 32 gray-scale image | W × 32 × 1 |
| Convolution | #maps:64, k:3 × 3, s:1, p:1 | W × 32 × 64 |
| MaxPooling | Window:2 × 2, s:2 | W/2 × 16 × 64 |
| Convolution | #maps:128, k:3 × 3, s:1, p:1 | W/2 × 16 × 128 |
| MaxPooling | Window:2 × 2, s:2 | W/4 × 8 × 128 |
| Convolution | #maps:256, k:3 × 3, s:1, p:1 | W/4 × 8 × 256 |
| Convolution | #maps:256, k:3 × 3, s:1, p:1 | W/4 × 8 × 256 |
| MaxPooling | Window:1 × 2, s:2 | W/4 × 4 × 256 |
| Convolution | #maps:512, k:3 × 3, s:1, p:1 | W/4 × 4 × 512 |
| BatchNormalization | - | W/4 × 4 × 512 |
| Convolution | #maps:512, k:3 × 3, s:1, p:1 | W/4 × 4 × 512 |
| BatchNormalization | - | W/4 × 4 × 512 |
| MaxPooling | Window:1 × 2, s:2 | W/4 × 2 × 512 |
| Convolution | #maps:512, k:2 × 2, s:1, p:0 | W/4-1 × 1 × 512 |
| Map-to-Sequence | - | W/4-1 × 512 |
| Bidirectional-LSTM | #hidden units:256 | W/4-1 × 256 |
| Bidirectional-LSTM | #hidden units:256 | W/4-1 × label_num |
| Transcription | - | str |
- Python 3
- PyTorch 1.0
Click here and download IIIT-5K dataset to the 'data/' folder of current path.
python main.py --fix_depthYou can tune the hyperparameters for better performence.
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