Unified text detection and recognition

First of all, thanks for the author of crnn and ctpn algorithm with opening their codes. Here is crnn original codes, here is ctpn original codes and a tensorflow version can be found here

Ubuntu16.04(it's more appropriate to build warp-ctc than other)

python3

tensorflow-gpu(version>=1.6)

pytorch(version>=0.3)

warp-ctc click here to download, follow the author to build warp-ctc

cuda 9.0 and cudnn 7.0

Just using pip to establish your dev environment, eg. pip install tensorflow-gpu==1.6, pip install torchvesion(it will install pytorch meanwhile)

I share my models of crnn and ctpn, you can run it just for take a look of its effect.

Images and results are stored in detect_data file.

cd end2endDec_Rec

python3 ./ctpn_crnn_combined/end2endDet_Rec.py

following is detect images example

Here I just share the method that was used to train a crnn model. In my training, ultimately it reached an accuracy of 97.8%. Before training, you should generate a lmdb dataset. Click here for downloading a 3.6 million chinese characters training sets. Or just download the lmdb datasets that were generated by me.

Here are some examples about chinese characters training data.

if you want to create it by yourself, you can reference dataset_preprocess directory.

cd train_crnn

python3 crnn_main.py --trainroot lmdb_data_set/xxx --valroot lmdb_data_set/xxx (--cuda)