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| import tensorflow as tf | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| from huggingface_hub import from_pretrained_keras | |
| import numpy as np | |
| import gradio as gr | |
| max_length = 5 | |
| img_width = 200 | |
| img_height = 50 | |
| model = from_pretrained_keras("keras-io/ocr-for-captcha", compile=False) | |
| prediction_model = keras.models.Model( | |
| model.get_layer(name="image").input, model.get_layer(name="dense2").output | |
| ) | |
| with open("vocab.txt", "r") as f: | |
| vocab = f.read().splitlines() | |
| # Mapping integers back to original characters | |
| num_to_char = layers.StringLookup( | |
| vocabulary=vocab, mask_token=None, invert=True | |
| ) | |
| def decode_batch_predictions(pred): | |
| input_len = np.ones(pred.shape[0]) * pred.shape[1] | |
| # Use greedy search. For complex tasks, you can use beam search | |
| results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][ | |
| :, :max_length | |
| ] | |
| # Iterate over the results and get back the text | |
| output_text = [] | |
| for res in results: | |
| res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8") | |
| output_text.append(res) | |
| return output_text | |
| def classify_image(img_path): | |
| # 1. Read image | |
| img = tf.io.read_file(img_path) | |
| # 2. Decode and convert to grayscale | |
| img = tf.io.decode_png(img, channels=1) | |
| # 3. Convert to float32 in [0, 1] range | |
| img = tf.image.convert_image_dtype(img, tf.float32) | |
| # 4. Resize to the desired size | |
| img = tf.image.resize(img, [img_height, img_width]) | |
| # 5. Transpose the image because we want the time | |
| # dimension to correspond to the width of the image. | |
| img = tf.transpose(img, perm=[1, 0, 2]) | |
| img = tf.expand_dims(img, axis=0) | |
| preds = prediction_model.predict(img) | |
| pred_text = decode_batch_predictions(preds) | |
| return pred_text[0] | |
| image = gr.inputs.Image(type='filepath') | |
| text = gr.outputs.Textbox() | |
| iface = gr.Interface(classify_image,image,text, | |
| title="OCR for CAPTCHA", | |
| description = "Keras Implementation of OCR model for reading captcha 🤖🦹🏻", | |
| article = "Author: <a href=\"https://huggingface.co/anuragshas\">Anurag Singh</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/captcha_ocr/\">A_K_Nain</a>", | |
| examples = ["dd764.png","3p4nn.png"] | |
| ) | |
| iface.launch() | |