Hi, Thanks for sharing the great work!

I downloaded the data based on the suggestion of this link.

Then I tried running the training script, but encountered below error,

    train_feeder = utils.DataIterator(data_dir=train_dir)
  File "/home/levin/workspace/snrprj/CNN_LSTM_CTC_Tensorflow/utils.py", line 73, in __init__
    code = image_name.split('/')[-1].split('_')[1].split('.')[0]
IndexError: list index out of range

It looks to me that the script expects to get label for each image from its filename. So to get the code run properly and train the model, we will have to first rename the image files based on the labels.txt file, is this correct?

在网上看过您的CNN_LSTM_CTC_Tensorflow 源码，也下载了数据集，想重现您的结果，有几个问题请教一下，谢谢！ 1，源码是在这里下载的，https://github.com/watsonyanghx/CNN_LSTM_CTC_Tensorflow，数据集也下载解压了。 D:\Tensorflow\CNN_LSTM_CTC_Tensorflow-master\imgs 解压后目录结构 imgs\labels.txt imgs\image_contest_level_1\

2.运行 helper.py 后，在imgs 目录下生成了 X_train.txt、 X_val.txt、 y_train.txt、 y_val.txt4个文件是正常的。

X_train.txt 训练的文件名 X_val.txt 测试的文件名

y_train.txt 训练的答案 y_val.txt 测试的答案

但 cp_file(X_train, y_train, './imgs/train/') cp_file(X_val, y_val, './imgs/val/') "D:\Program Files\Python365\python36.exe" D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/helper.py ['./imgs/image_contest_level_1/0.png' './imgs/image_contest_level_1/1.png' './imgs/image_contest_level_1/2.png' './imgs/image_contest_level_1/3.png' './imgs/image_contest_level_1/4.png' './imgs/image_contest_level_1/5.png' './imgs/image_contest_level_1/6.png' './imgs/image_contest_level_1/7.png' './imgs/image_contest_level_1/8.png' './imgs/image_contest_level_1/9.png'] Traceback (most recent call last): File "D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/helper.py", line 129, in cp_file(X_train, y_train, './imgs/train/') File "D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/helper.py", line 102, in cp_file shutil.copyfile(file_path, dest_filename) File "D:\Program Files\Python365\lib\shutil.py", line 121, in copyfile with open(dst, 'wb') as fdst: OSError: [Errno 22] Invalid argument: './imgs/train/73091_(8+9)*4.png'

进程完成，退出码 1 文件名中 73091_(8+9)*4.png 含有特殊字符，是不能命名成功的，不知道您是怎么处理的

3.运行 cnn_lstm_otc_ocr.py 报错

"D:\Program Files\Python365\python36.exe" D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/cnn_lstm_otc_ocr.py D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/cnn_lstm_otc_ocr.py:42: SyntaxWarning: assertion is always true, perhaps remove parentheses? assert (FLAGS.cnn_count <= count_, "FLAGS.cnn_count should be <= {}!".format(count_))

4.运行 main.py

"D:\Program Files\Python365\python36.exe" D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/main.py

feature_h: 4, feature_w: 12 lstm input shape: [40, 12, 256] loading train data size: 0 loading validation data size: 0

2018-07-12 11:02:14.624545: I c:\users\user\source\repos\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 2018-07-12 11:02:14.844809: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.683 pciBusID: 0000:01:00.0 totalMemory: 8.00GiB freeMemory: 6.63GiB 2018-07-12 11:02:14.845239: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0 2018-07-12 11:02:16.119318: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix: 2018-07-12 11:02:16.119683: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929] 0 2018-07-12 11:02:16.119937: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0: N 2018-07-12 11:02:16.137500: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6410 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1) =============================begin training=============================

进程完成，退出码 0

5. 运行 utils.py "D:\Program Files\Python365\python36.exe" D:/Tensorflow/CNN_LSTM_CTC_Tensorflow-master/utils.py

进程完成，退出码 0

谢谢指点，不知您是否有微信或qq方便联系，请教学习，谢谢

Hello,I chang the Image width and height from(60,180)to(80,500),then I get an error:

InvalidArgumentError (see above for traceback): Matrix size-incompatible: In[0]: [40,288], In[1]: [176,512] [[Node: lstm/rnn/while/multi_rnn_cell/cell_0/lstm_cell/lstm_cell/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](lstm/rnn/while/multi_rnn_cell/cell_0/lstm_cell/lstm_cell/concat, lstm/rnn/while/multi_rnn_cell/cell_0/lstm_cell/lstm_cell/MatMul/Enter)]] [[Node: Mean/_37 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_950_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]]

Is there anything else I should change to fix this error?

I trained the model with a custom dataset and got the checkpoint files. I froze the model using this script

import tensorflow as tf
def freeze_graph(model_dir, output_node_names, frozen_graph_name):
    if not tf.gfile.Exists(model_dir):
        raise AssertionError(
            "Export directory doesn't exists. Please specify an export "
            "directory: %s" % model_dir)

    if not output_node_names:
        print("You need to supply the name of a node to --output_node_names.")
        return -1

    # We retrieve our checkpoint fullpath
    checkpoint = tf.train.get_checkpoint_state(model_dir)
    input_checkpoint = checkpoint.model_checkpoint_path

    # We precise the file fullname of our freezed graph
    absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1])
    output_graph = absolute_model_dir + "/" + frozen_graph_name + ".pb"

    # We clear devices to allow TensorFlow to control on which device it will load operations
    clear_devices = True

    # We start a session using a temporary fresh Graph
    with tf.Session(graph=tf.Graph()) as sess:
        # We import the meta graph in the current default Graph
        saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)

        # We restore the weights
        saver.restore(sess, input_checkpoint)
        gd = sess.graph.as_graph_def()
        # We use a built-in TF helper to export variables to constants
        output_graph_def = tf.graph_util.convert_variables_to_constants(
            sess,  # The session is used to retrieve the weights
            gd,  # The graph_def is used to retrieve the nodes
            output_node_names.split(",")  # The output node names are used to select the usefull nodes
        )

        # Finally we serialize and dump the output graph to the filesystem
        with tf.gfile.GFile(output_graph, "wb") as f:
            f.write(output_graph_def.SerializeToString())
        print("%d ops in the final graph." % len(output_graph_def.node))

    return output_graph_def

freeze_graph('./checkpoint','SparseToDense','ocr.pb')

But when I'm loading the graph from the protobuf file, I'm getting this error:

ValueError: Input 0 of node import/cnn/unit-4/bn4/BatchNorm/AssignMovingAvg/cnn/unit-4/bn4/BatchNorm/moving_mean/AssignAdd was passed float from import/cnn/unit-4/bn4/BatchNorm/cnn/unit-4/bn4/BatchNorm/moving_mean/local_step:0 incompatible with expected float_ref.

I know this is a little off topic but any help is appreciated.

Hi Guys

I have prepared a small dataset just for trying out the network and see how it works. It seems like that its able to load the data set well and prints (Begin Training) but after that it just stops and do nothing.Here is what i see on screen: CUDA_VISIBLE_DEVICES=0 python ./main.py --train_dir=./imgs/train/ --val_dir=./imgs/val/ --image_height=60 --image_width=180 --image_channel=1 --out_channels=64 --num_hidden=128 --batch_size=128 --log_dir=./log/train --num_gpus=1 --mode=train

feature_h: 4, feature_w: 12 lstm input shape: [128, 12, 256] loading train data ('size: ', 11) loading validation data size: 6

2018-05-29 11:47:19.300427: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2018-05-29 11:47:19.954690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2018-05-29 11:47:19.955398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties: name: GeForce GTX 960M major: 5 minor: 0 memoryClockRate(GHz): 1.176 pciBusID: 0000:01:00.0 totalMemory: 3.95GiB freeMemory: 3.50GiB 2018-05-29 11:47:19.955416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0 2018-05-29 11:47:20.485722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix: 2018-05-29 11:47:20.485760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] 0 2018-05-29 11:47:20.485768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0: N 2018-05-29 11:47:20.485968: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3237 MB memory) -> physical GPU (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0) =============================begin training============================= as you can see Training does not begin and i dont get any errors either

got ctc cost nan error after 30 epoch in chinese sentence ocr training... I can delay the error by smaller lr, bigger lr decay. But how to prevent ctc cost nan?

Hi, I try to train but using this cmd command:

main.py --train_dir=../imgs/train/ --val_dir=../imgs/val/ --image_height=60 --image_width=180 --image_channel=1 --out_channels=64 --num_hidden=128 --batch_size=128 --log_dir=./log/train --num_gpus=1 -mode=train

But got this error:

Traceback (most recent call last):
  File "C:\Projects\CNN_LSTM_CTC_Tensorflow\main.py", line 14, in <module>
    import cnn_lstm_otc_ocr
  File "C:\Projects\CNN_LSTM_CTC_Tensorflow\cnn_lstm_otc_ocr.py", line 6, in <module>
    import utils
  File "C:\Projects\CNN_LSTM_CTC_Tensorflow\utils.py", line 43, in <module>
    tf.app.flags.DEFINE_string('log_dir', './log', 'the logging dir')
  File "C:\Users\N1throServer\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\platform\flags.py", line 58, in wrapper
    return original_function(*args, **kwargs)
  File "C:\Users\N1throServer\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\flags\_defines.py", line 241, in DEFINE_string
    DEFINE(parser, name, default, help, flag_values, serializer, **args)
  File "C:\Users\N1throServer\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\flags\_defines.py", line 82, in DEFINE
    flag_values, module_name)
  File "C:\Users\N1throServer\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\flags\_defines.py", line 104, in DEFINE_flag
    fv[flag.name] = flag
  File "C:\Users\N1throServer\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\flags\_flagvalues.py", line 430, in __setitem__
    raise _exceptions.DuplicateFlagError.from_flag(name, self)
absl.flags._exceptions.DuplicateFlagError: The flag 'log_dir' is defined twice. First from absl.logging, Second from utils.  Description from first occurrence: directory to write logfiles into

How can it be fixed?

Hi, Dear All, thanks a lot for this great project! I have trained the model with 32x128 OCR images successfully. I have a question, how do we test the new test images with the model? Using sliding window? I mean generally speaking, the images detected from the previous text detection branch are variable lengths, how do we input these images into the model to get the prediction? I thought about the sliding window, but could you please provide some advice or reference papers on this? Thanks.

I strated trainning the model and i stoped it manually via keyboard exception to test the inference but when i run the command i get no errors and nothing happens?

Hi @watsonyanghx i found your code and i think i'm going to use it for license plate ocr but i want to ask first : In the inference mode do the images i want to test in mode infer have to have labels?