I am currently trying to train the model fro detecting handwritten words in Farsi language which is in nature cursive. After some amount of time I get absolutely 0.00 acc and all the predictions from the beginning to the end remains the same. This means there happens absolutely no training for the model. I have checked every steps that I have taken and have no idea what I am doing wrong. Is it possible to train this model for another language at all?

My second question is how can I set the training to be happening on GPU. I have a GPU and it is working fine on other training procedures on my machine but this code runs only on CPU. Is there any flag or something for this?

My last question is for the char list number. according to the decoder on English language the length of chars set must be 79 and if this condition does not satisfies the Tensorflow throws error which as far as I traced it relates to this. How can I make this suitable for any number of chars?

I'm not sure the title is tremendously surprising to anyone, but I cleared 76% word accuracy with a deeper network. More interestingly, using a deeper network and terminating around epoch 25 yields a 74-75% word accuracy model, which is better and faster than training a smaller network to the bitter end.

Relevant code:

		for i in range(numLayers):
			kernel = tf.Variable(tf.truncated_normal([kernelVals[i], kernelVals[i], featureVals[i], featureVals[i + 1]], stddev=0.1))
			conv = tf.nn.conv2d(pool, kernel, padding='SAME',  strides=(1,1,1,1))
			conv_norm = tf.layers.batch_normalization(conv, training=self.is_train)
			relu = tf.nn.relu(conv_norm)
			kernel2 = tf.Variable(tf.truncated_normal([kernelVals[i], kernelVals[i], featureVals[i+1], featureVals[i + 1]], stddev=0.1))
			conv2 = tf.nn.conv2d(relu, kernel2, padding='SAME',  strides=(1,1,1,1))
			conv_norm2 = tf.layers.batch_normalization(conv2, training=self.is_train)
			relu2 = tf.nn.relu(conv_norm2)
			pool = tf.nn.max_pool(relu2, (1, poolVals[i][0], poolVals[i][1], 1), (1, strideVals[i][0], strideVals[i][1], 1), 'VALID')

I've currently been using SimpleHTR in my day to day work, and the WBS functionality has been very useful. The issue is this: I'm using it mainly for the Thai language, where there are no spaces between words within a sentence. Something weird happens in the below sentence. Actual label: คุณต้องระวังอะไรเป็นพิเศษ? (the words in this sentence are: คุณ ต้อง ระวัง อะไร เป็น พิเศษ)

Bestpath prediction: คุณต้องระวังอะไรเป็นพิเศษ7

WBS prediction: คุณ.ต้อง-ระวัง%มร(ปีน9เศษ7

As you might be able to see, the words predicted are generally correct, but there are non-word characters introduced between them as if they were stopword characters or punctuation. Is there any workaround for this? I understand that wbs was likely designed with latin languages with natural gaps between words in mind, but is there a way to not force the introduction of nonword characters as punctuation for other languages?

Hi Harald,

Thank you for this starter in OCR NN. Is there any reason why you didn't include batch normalization layer between conv2d and relu?

Regards,

Hi, Is this model only works for single handwritten word? When the image has a collection of multiple words (a sentence) then this model gives poor/no result. But when the image has only one word like (Cat), this model works perfectly. Is my observation is correct or am I missing something here? Thanks.

Hello Harald,

I am trying to train the model with some new IAM data, but before doing that I tried training with just the base dataset I was directed to in the README. The accuracy of the model changed from ~10% error rate in the GitHub model here, to ~43% error rate (according to accuracy.txt).

I ran into similar problems on:

• Tensorflow 1.10, Python 3.6.5, Ubuntu 16.04.5 LTS
• Tensorflow 1.10, Python 3.7.2, MacOS 10.13

I got this high error rate just by following the training instructions in the README. Hope to hear back from you soon on how you produced a more accurate model.

Thanks for your great work!

https://github.com/githubharald/SimpleHTR/blob/7f26b321f8b8b18e5f60cbc1b7d5e1ad202e7487/src/main.py#L177

From my point of view validate should use char_list from the model instead from the loaded dataset. This way if the user uses a different dataset for validation it will work. In current implmentation, the charList depends of dataset and will create errors if the new dataset has not exactly same charlist as the learning dataset.

Hello sir , Thanks for sharing the repo with us . may you please let me know , that you trained on word images or line images (of IAM) if Model size is 800*64(Used in your thesis )(FAQ article)?? This would be great help. I tried to train on word images even after 24 Hours Word Accuracy is 1% so i thought i should try to train over line images. Kindly guide

Traceback (most recent call last): File "main.py", line 139, in main() File "main.py", line 115, in main loader = DataLoader(FilePaths.fnTrain, Model.batchSize, Model.imgSize, Model.maxTextLen) File "/home/syedjafer/Documents/FinalYearProj/SimpleHTR/src/DataLoader.py", line 43, in init assert (len(lineSplit) >= 9) , "Working" AssertionError: Working

I'm trying to run python main.py --train with a new image data. But I got the error:

Init with new values Epoch: 1 Train NN Validate NN Traceback (most recent call last): File "main.py", line 142, in main() File "main.py", line 129, in main train(model, loader) File "main.py", line 39, in train charErrorRate = validate(model, loader) File "main.py", line 84, in validate charErrorRate = numCharErr / numCharTotal ZeroDivisionError: division by zero

How can I solve it?

Python: 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49) [GCC 7.2.0] Tensorflow: 1.8.0 2018-06-25 17:43:33.775081: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

Traceback (most recent call last): File "main.py", line 97, in infer(fnInfer) File "main.py", line 86, in infer model = Model(open(fnCharList).read(), mustRestore=True) File "/home/jatinpal/goall/SimpleHTR/src/Model.py", line 33, in init (self.sess, self.saver) = self.setupTF() File "/home/jatinpal/goall/SimpleHTR/src/Model.py", line 106, in setupTF raise Exception('No saved model found in: ' + modelDir) Exception: No saved model found in: ../model/

can anyone suggest why I an not able to locate the saved model . My laptop details ubuntu 16.04,8GB RAM, python 3.6.3(anaconda) tensorflow 1.8.0 cv2 3.4.1