Building an OCR Model to Crack Captchas: A Neural Network Tutorial with Keras and TensorFlow 

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For anyone who is getting poor results: 1. The small dataset means that a random split might not generalise the problem. for example, the train dataset might contain much higher percentage of a digit than another 2. You can use opencv to perform preprocessing which can improve performance. Using morphological transformations to remove noise can improve performance immensely. 3. To avoid overfitting, I found that a Gaussian noise layer can help. This makes it harder to learn therefore harder to overfit. Hope this helps!

Hi Nicolai, I was wondering would there be a way to feed in this kind of network wider images with text or have kind of dynamic input with size?

Hi Nicolai, thanks for great explanation. Could you please explain how to measure accuracy?

where is the repository link i am not able to find it in description

Hi Nicola When i add "num_oov_indices" = 0 parameter in stringLookup code then model training code work but it post labels on wrong images in visualization part before training and creating model. So i removed "num_oov_indices" and now my model training code of earlystopping is not working. Code stop in very first epoch Any solution for this ?

I m trying to run this code but m getting error like InvalidArgumentError : graph execution error Anyone can help with this

Hey, Thank you very much for this beautiful explanation of the code and the philosophy behind ocr with LSTM and CTC layer. Can you please verify if the code always works well because I was executing it and it was working but now doesn't. I think there is a problem in mapping characters to numbers and mapping numbers to their original characters by the function of ('' layers.experimental.preprocessing.StringLookup''). I tried to compilate it in google colab but when I tried to visualize the data it doesn't give the correct label text. I would be very thankful if you verify it and give some solutions to fIxe the problem of mapping characters to numbers and mapping numbers to their original characters .

Do you think I can use your code to decode the digits of my water counter?

## Preprocessing # Mapping characters to integers char_to_num = layers.experimental.preprocessing.StringLookup( vocabulary=list(characters), mask_token=None ) # Mapping integers back to original characters num_to_char = layers.experimental.preprocessing.StringLookup( vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True

Dear Coding Lib! im here with the Capthcha project! seems like turning the shuffle on messes with the shuffling function and does incorrect tplit. I have yet to find solution, and would really appreciate if you looked into it! If shuffle is off, it works well. Another person pointed the bug out, and its labels being on wrong images

Can I use this for model for license plates?

can anyone provide me wiyh the code

can you provide library versions you used

can i extract text from images by the way ? My final project is extract text from images but i can not coding . I need to help please .

thanks a lot !

Excuse me bro, i have an issue when im running build_model() function after CTC Loss its happen in line 43 about x = layers.Reshape(target_shape = new_shape, name='reshape')(x) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () 73 74 # Panggil Functionnya buat bkin model ---> 75 model = build_model() 76 model.summary() in build_model() 41 # floor division menghasilkan nilai berupa hasil dari pembagian bersisa 42 new_shape = ((img_width // 4), (img_height // 4) * 64) ---> 43 x = layers.Reshape(target_shape = new_shape, name='reshape')(x) 44 x = layers.Dense(64, activation='relu', name='dense1')(x) 45 x = layers.Dropout(0.2)(x) /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs) 975 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list): 976 return self._functional_construction_call(inputs, args, kwargs, --> 977 input_list) 978 979 # Maintains info about the `Layer.call` stack. /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list) 1113 # Check input assumptions set after layer building, e.g. input shape. 1114 outputs = self._keras_tensor_symbolic_call( -> 1115 inputs, input_masks, args, kwargs) 1116 1117 if outputs is None: /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs) 846 return tf.nest.map_structure(keras_tensor.KerasTensor, output_signature) 847 else: --> 848 return self._infer_output_signature(inputs, args, kwargs, input_masks) 849 850 def _infer_output_signature(self, inputs, args, kwargs, input_masks): /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks) 886 self._maybe_build(inputs) 887 inputs = self._maybe_cast_inputs(inputs) --> 888 outputs = call_fn(inputs, *args, **kwargs) 889 890 self._handle_activity_regularization(inputs, outputs) /usr/local/lib/python3.7/dist-packages/keras/layers/core.py in call(self, inputs) 537 # Set the static shape for the result since it might lost during array_ops 538 # reshape, eg, some `None` dim in the result could be inferred. --> 539 result.set_shape(self.compute_output_shape(inputs.shape)) 540 return result 541 /usr/local/lib/python3.7/dist-packages/keras/layers/core.py in compute_output_shape(self, input_shape) 528 output_shape = [input_shape[0]] 529 output_shape += self._fix_unknown_dimension(input_shape[1:], --> 530 self.target_shape) 531 return tf.TensorShape(output_shape) 532 /usr/local/lib/python3.7/dist-packages/keras/layers/core.py in _fix_unknown_dimension(self, input_shape, output_shape) 516 output_shape[unknown] = original // known 517 elif original != known: --> 518 raise ValueError(msg) 519 return output_shape 520 --------------------------------------------------------------------------- and this the error message ValueError: total size of new array must be unchanged, input_shape = [50, 50, 64], output_shape = [50, 768]

can it suitable for text recognition task?

Hey i am not getting accurate results, i checked your github for some reason the labels arent matching the captchas during testing what would you recommend to do

Hi, I am a beginner in this field and I've watched your video and implemented this code. Its working fine but I need to test a single captcha image how can I do that. I was trying to do that but the prediction was not good . Please help me out if you can. 🥺

Sir Why we can't use regular objects detection to detect the number ?

Hi! Im trying the code but i having an error while training : Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [5], [batch]: [6]. Anyone can help me to fix this?

Hi, how can I get for captcha that has 6 digits each picture? Currently it’s 5 digits in your example, I know I need to change something in the model but I can’t seem to figure it out, :( the error I keep getting is cannot add tensor to batch. Number of elements does not match. Shapes are: [tensor]: [5] [batch]: [6] How should I change or how do I understand what I need to change?

how to impliment it???You just read all docs

How to crack 6 digits and characters captcha

Anyone else has the same error?: File "C:\Users\user\PycharmProjects\ocr_gas\ocr.py", line 135, in call * label_length = tf.cast(tf.shape(y_true)[1], dtype="int64") ValueError: slice index 1 of dimension 0 out of bounds. for '{{node ocr_model_v1/ctc_loss/strided_slice_2}} = StridedSlice[Index=DT_INT32, T=DT_INT32, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=1](ocr_model_v1/ctc_loss/Shape_2, ocr_model_v1/ctc_loss/strided_slice_2/stack, ocr_model_v1/ctc_loss/strided_slice_2/stack_1, ocr_model_v1/ctc_loss/strided_slice_2/stack_2)' with input shapes: [1], [1], [1], [1] and with computed input tensors: input[1] = , input[2] = , input[3] = . Call arguments received by layer "ctc_loss" " f"(type CTCLayer): • y_true=tf.Tensor(shape=(None,), dtype=float32) • y_pred=tf.Tensor(shape=(None, 50, 12), dtype=float32)