430 lines
13 KiB
Python
430 lines
13 KiB
Python
"""
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Title: OCR model for reading Captchas
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Author: [A_K_Nain](https://twitter.com/A_K_Nain)
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Date created: 2020/06/14
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Last modified: 2024/03/13
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Description: How to implement an OCR model using CNNs, RNNs and CTC loss.
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Accelerator: GPU
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Converted to Keras 3 by: [Sitam Meur](https://github.com/sitamgithub-MSIT)
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"""
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"""
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## Introduction
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This example demonstrates a simple OCR model built with the Functional API. Apart from
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combining CNN and RNN, it also illustrates how you can instantiate a new layer
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and use it as an "Endpoint layer" for implementing CTC loss. For a detailed
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guide to layer subclassing, please check out
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[this page](https://keras.io/guides/making_new_layers_and_models_via_subclassing/)
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in the developer guides.
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"""
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"""
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## Setup
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"""
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import os
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os.environ["KERAS_BACKEND"] = "tensorflow"
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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import tensorflow as tf
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import keras
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from keras import ops
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from keras import layers
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"""
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## Load the data: [Captcha Images](https://www.kaggle.com/fournierp/captcha-version-2-images)
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Let's download the data.
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"""
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"""shell
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curl -LO https://github.com/AakashKumarNain/CaptchaCracker/raw/master/captcha_images_v2.zip
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unzip -qq captcha_images_v2.zip
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"""
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"""
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The dataset contains 1040 captcha files as `jpeg` images. The label for each sample is a string,
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the name of the file (minus the file extension).
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We will map each character in the string to an integer for training the model. Similary,
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we will need to map the predictions of the model back to strings. For this purpose
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we will maintain two dictionaries, mapping characters to integers, and integers to characters,
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respectively.
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"""
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# Path to the data directory
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data_dir = Path("./datasets/training")
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# Get list of all the images
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images = sorted(list(map(str, list(data_dir.glob("*.jpeg")))))
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labels = [img.split(os.path.sep)[-1].split(".jpeg")[0].split("_")[1].upper() for img in images]
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characters = set(char for label in labels for char in label)
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characters = sorted(list(characters))
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print("Number of images found: ", len(images))
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print("Number of labels found: ", len(labels))
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print("Number of unique characters: ", len(characters))
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print("Characters present: ", characters)
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# Batch size for training and validation
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batch_size = 16
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# Desired image dimensions
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img_width = 200
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img_height = 70
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# Factor by which the image is going to be downsampled
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# by the convolutional blocks. We will be using two
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# convolution blocks and each block will have
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# a pooling layer which downsample the features by a factor of 2.
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# Hence total downsampling factor would be 4.
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downsample_factor = 4
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# Maximum length of any captcha in the dataset
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# print([len(label) for label in labels])
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max_length = max([len(label) for label in labels])
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"""
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## Preprocessing
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"""
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# Mapping characters to integers
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char_to_num = layers.StringLookup(vocabulary=list(characters), mask_token=None)
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# Mapping integers back to original characters
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num_to_char = layers.StringLookup(
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vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
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)
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def split_data(images, labels, train_size=0.9, shuffle=True):
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# 1. Get the total size of the dataset
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size = len(images)
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# 2. Make an indices array and shuffle it, if required
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indices = ops.arange(size)
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if shuffle:
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indices = keras.random.shuffle(indices)
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# 3. Get the size of training samples
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train_samples = int(size * train_size)
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# 4. Split data into training and validation sets
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x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]
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x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]
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return x_train, x_valid, y_train, y_valid
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# Splitting data into training and validation sets
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x_train, x_valid, y_train, y_valid = split_data(np.array(images), np.array(labels))
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def encode_single_sample(img_path, label):
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# 1. Read image
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img = tf.io.read_file(img_path)
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# 2. Decode and convert to grayscale
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img = tf.io.decode_jpeg(img, channels=1)
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# 3. Convert to float32 in [0, 1] range
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img = tf.image.convert_image_dtype(img, tf.float32)
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# 4. Resize to the desired size
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img = ops.image.resize(img, [img_height, img_width])
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# 5. Transpose the image because we want the time
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# dimension to correspond to the width of the image.
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img = ops.transpose(img, axes=[1, 0, 2])
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# 6. Map the characters in label to numbers
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label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
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# 7. Return a dict as our model is expecting two inputs
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return {"image": img, "label": label}
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"""
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## Create `Dataset` objects
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"""
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train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
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train_dataset = (
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train_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)
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.batch(batch_size)
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.prefetch(buffer_size=tf.data.AUTOTUNE)
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)
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validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))
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validation_dataset = (
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validation_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)
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.batch(batch_size)
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.prefetch(buffer_size=tf.data.AUTOTUNE)
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)
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"""
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## Visualize the data
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"""
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_, ax = plt.subplots(4, 4, figsize=(10, 5))
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for batch in train_dataset.take(1):
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images = batch["image"]
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labels = batch["label"]
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for i in range(16):
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img = (images[i] * 255).numpy().astype("uint8")
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label = tf.strings.reduce_join(num_to_char(labels[i])).numpy().decode("utf-8")
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ax[i // 4, i % 4].imshow(img[:, :, 0].T, cmap="gray")
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ax[i // 4, i % 4].set_title(label)
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ax[i // 4, i % 4].axis("off")
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plt.show()
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"""
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## Model
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"""
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def ctc_batch_cost(y_true, y_pred, input_length, label_length):
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label_length = ops.cast(ops.squeeze(label_length, axis=-1), dtype="int32")
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input_length = ops.cast(ops.squeeze(input_length, axis=-1), dtype="int32")
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sparse_labels = ops.cast(
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ctc_label_dense_to_sparse(y_true, label_length), dtype="int32"
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)
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y_pred = ops.log(ops.transpose(y_pred, axes=[1, 0, 2]) + keras.backend.epsilon())
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return ops.expand_dims(
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tf.compat.v1.nn.ctc_loss(
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inputs=y_pred, labels=sparse_labels, sequence_length=input_length
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),
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1,
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)
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def ctc_label_dense_to_sparse(labels, label_lengths):
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label_shape = ops.shape(labels)
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num_batches_tns = ops.stack([label_shape[0]])
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max_num_labels_tns = ops.stack([label_shape[1]])
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def range_less_than(old_input, current_input):
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return ops.expand_dims(ops.arange(ops.shape(old_input)[1]), 0) < tf.fill(
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max_num_labels_tns, current_input
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)
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init = ops.cast(tf.fill([1, label_shape[1]], 0), dtype="bool")
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dense_mask = tf.compat.v1.scan(
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range_less_than, label_lengths, initializer=init, parallel_iterations=1
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)
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dense_mask = dense_mask[:, 0, :]
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label_array = ops.reshape(
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ops.tile(ops.arange(0, label_shape[1]), num_batches_tns), label_shape
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)
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label_ind = tf.compat.v1.boolean_mask(label_array, dense_mask)
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batch_array = ops.transpose(
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ops.reshape(
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ops.tile(ops.arange(0, label_shape[0]), max_num_labels_tns),
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tf.reverse(label_shape, [0]),
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)
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)
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batch_ind = tf.compat.v1.boolean_mask(batch_array, dense_mask)
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indices = ops.transpose(
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ops.reshape(ops.concatenate([batch_ind, label_ind], axis=0), [2, -1])
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)
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vals_sparse = tf.compat.v1.gather_nd(labels, indices)
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return tf.SparseTensor(
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ops.cast(indices, dtype="int64"),
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vals_sparse,
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ops.cast(label_shape, dtype="int64"),
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)
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class CTCLayer(layers.Layer):
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def __init__(self, name=None):
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super().__init__(name=name)
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self.loss_fn = ctc_batch_cost
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def call(self, y_true, y_pred):
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# Compute the training-time loss value and add it
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# to the layer using `self.add_loss()`.
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batch_len = ops.cast(ops.shape(y_true)[0], dtype="int64")
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input_length = ops.cast(ops.shape(y_pred)[1], dtype="int64")
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label_length = ops.cast(ops.shape(y_true)[1], dtype="int64")
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input_length = input_length * ops.ones(shape=(batch_len, 1), dtype="int64")
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label_length = label_length * ops.ones(shape=(batch_len, 1), dtype="int64")
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loss = self.loss_fn(y_true, y_pred, input_length, label_length)
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self.add_loss(loss)
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# At test time, just return the computed predictions
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return y_pred
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def build_model():
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# Inputs to the model
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input_img = layers.Input(
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shape=(img_width, img_height, 1), name="image", dtype="float32"
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)
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labels = layers.Input(name="label", shape=(None,), dtype="float32")
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# First conv block
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x = layers.Conv2D(
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32,
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(3, 3),
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activation="relu",
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kernel_initializer="he_normal",
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padding="same",
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name="Conv1",
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)(input_img)
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x = layers.MaxPooling2D((2, 2), name="pool1")(x)
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# Second conv block
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x = layers.Conv2D(
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64,
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(3, 3),
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activation="relu",
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kernel_initializer="he_normal",
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padding="same",
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name="Conv2",
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)(x)
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x = layers.MaxPooling2D((2, 2), name="pool2")(x)
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# We have used two max pool with pool size and strides 2.
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# Hence, downsampled feature maps are 4x smaller. The number of
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# filters in the last layer is 64. Reshape accordingly before
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# passing the output to the RNN part of the model
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new_shape = ((img_width // 4), (img_height // 4) * 64)
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x = layers.Reshape(target_shape=new_shape, name="reshape")(x)
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x = layers.Dense(64, activation="relu", name="dense1")(x)
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x = layers.Dropout(0.2)(x)
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# RNNs
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x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)
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x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)
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# Output layer
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x = layers.Dense(
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len(char_to_num.get_vocabulary()) + 1, activation="softmax", name="dense2"
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)(x)
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# Add CTC layer for calculating CTC loss at each step
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output = CTCLayer(name="ctc_loss")(labels, x)
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# Define the model
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model = keras.models.Model(
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inputs=[input_img, labels], outputs=output, name="ocr_model_v1"
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)
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# Optimizer
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opt = keras.optimizers.Adam()
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# Compile the model and return
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model.compile(optimizer=opt)
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return model
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# Get the model
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model = build_model()
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model.summary()
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"""
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## Training
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"""
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# TODO restore epoch count.
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epochs = 100
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early_stopping_patience = 10
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# Add early stopping
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early_stopping = keras.callbacks.EarlyStopping(
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monitor="val_loss", patience=early_stopping_patience, restore_best_weights=True
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)
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# Train the model
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history = model.fit(
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train_dataset,
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validation_data=validation_dataset,
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epochs=epochs,
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callbacks=[early_stopping],
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)
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"""
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## Inference
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You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/ocr-for-captcha)
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and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/ocr-for-captcha).
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"""
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def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
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input_shape = ops.shape(y_pred)
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num_samples, num_steps = input_shape[0], input_shape[1]
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y_pred = ops.log(ops.transpose(y_pred, axes=[1, 0, 2]) + keras.backend.epsilon())
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input_length = ops.cast(input_length, dtype="int32")
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if greedy:
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(decoded, log_prob) = tf.nn.ctc_greedy_decoder(
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inputs=y_pred, sequence_length=input_length
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)
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else:
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(decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(
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inputs=y_pred,
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sequence_length=input_length,
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beam_width=beam_width,
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top_paths=top_paths,
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)
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decoded_dense = []
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for st in decoded:
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st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))
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decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))
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return (decoded_dense, log_prob)
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# Get the prediction model by extracting layers till the output layer
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prediction_model = keras.models.Model(
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model.input[0], model.get_layer(name="dense2").output
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)
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prediction_model.summary()
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# A utility function to decode the output of the network
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def decode_batch_predictions(pred):
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input_len = np.ones(pred.shape[0]) * pred.shape[1]
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# Use greedy search. For complex tasks, you can use beam search
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results = ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
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:, :max_length
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]
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# Iterate over the results and get back the text
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output_text = []
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for res in results:
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res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
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output_text.append(res)
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return output_text
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# Let's check results on some validation samples
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for batch in validation_dataset.take(1):
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batch_images = batch["image"]
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batch_labels = batch["label"]
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preds = prediction_model.predict(batch_images)
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pred_texts = decode_batch_predictions(preds)
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orig_texts = []
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for label in batch_labels:
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label = tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
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orig_texts.append(label)
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_, ax = plt.subplots(4, 4, figsize=(15, 5))
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for i in range(len(pred_texts)):
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img = (batch_images[i, :, :, 0] * 255).numpy().astype(np.uint8)
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img = img.T
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title = f"Prediction: {pred_texts[i]}"
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ax[i // 4, i % 4].imshow(img, cmap="gray")
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ax[i // 4, i % 4].set_title(title)
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ax[i // 4, i % 4].axis("off")
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plt.show()
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