From 0140f85444590aad87a9402213f300a039daf5dc Mon Sep 17 00:00:00 2001 From: Wei Tang Date: Fri, 6 Apr 2018 17:11:12 +0800 Subject: [PATCH] Update README.md --- README.md | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/README.md b/README.md index 38225b4..1f8bde3 100644 --- a/README.md +++ b/README.md @@ -1 +1,32 @@ # crnn-mxnet-chinese-text-recognition +This is an implementation of CRNN (CNN+LSTM+CTC) for chinese text recognition. + +## Building MXNet with warp-ctc +1. In order to use `mxnet.symbol.WarpCTC` layer, you need to first build Baidu's [warp-ctc](https://github.com/baidu-research/warp-ctc) library from source +2. Then build MXNet from source with warp-ctc config flags enabled. + +## Data Preparation +1. Download the [Synthetic Chinese Dataset](https://pan.baidu.com/s/1dFda6R3)(contributed by https://github.com/senlinuc/caffe_ocr) + This dataset contains almost 3.6 million synthetic chinese text images with 5,990 different categories. Each image has a length of 10 + characters. +2. Create train.txt and text.txt with the format like this: + image_name1 label1_1 label1_2 label1_3... + image_name2 label2_1 label2_2 label2_3... + +## Training +1. Revide the path of images and txt files in train.py +2. Run +``` +$ python train.py 2>&1 | tee log.txt +``` +3. After almost 19 epoches, you can get 99.0502% validation accuracy. +``` +2018-04-01 03:35:35,136 Epoch[18] Batch [25450] Speed: 53.10 samples/sec accuracy=0.988125 +2018-04-01 03:37:37,482 Epoch[18] Batch [25500] Speed: 52.31 samples/sec accuracy=0.986719 +2018-04-01 03:39:38,613 Epoch[18] Batch [25550] Speed: 52.84 samples/sec accuracy=0.989531 +2018-04-01 03:41:40,470 Epoch[18] Batch [25600] Speed: 52.52 samples/sec accuracy=0.987969 +2018-04-01 03:42:27,544 Epoch[18] Train-accuracy=0.988672 +2018-04-01 03:42:27,544 Epoch[18] Time cost=80796.510 +2018-04-01 03:42:27,610 Saved checkpoint to "./check_points/model-0019.params" +2018-04-01 05:34:43,096 Epoch[18] Validation-accuracy=0.990502 +``` -- GitLab