CRNN Scene Text Tecognition

Paper： An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Dataset

The Datasets generated by trdg。

You just need to generate the training and test datasets into two different directories.

Result

The amount of training data for all models is 10000.

Five Words with Noise	Single Word with Noise (trained with same Size 100x32)	Three Words with Background
Test Accuracy: 92.5%	Test Accuracy: 93.5%	Test Accuracy: 40.4%
batch_size=16, epoch=30	batch_size=32, epoch=30	batch_size=16, epoch=30

Comment

crnn_str_simple-ipynb is a simple CRNN implementation that can be used with a single word dataset with good results.

crnn_str.ipynb can be used to identify text images of different lengths, and can also modify parameters to select single channel (grayscale image) or three channel (RGB color image) for training and testing.

Use pretrained model

The pretrained models can be downloaded by Google Drive or Baidu Netdisk .

The document description is as follows:

crnn_str_multi_v.pth	crnn_str_background.pth	crnn_str_single_simple.pth
Trained by crnn_str.ipynb using 5-word dataset.	Trained bycrnn_str.ipynb using 3-word dataset with background.	Trained bt crnn_str_simple.ipynb using single word dataset.

References:

https://github.com/bgshih/crnn

https://github.com/meijieru/crnn.pytorch