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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="CGIP CAPTCHA RECOGNITION OCR", | |
description = "Keras Implementation of OCR model for reading captcha π€π¦Ήπ»", | |
examples = ["dd764.png","3p4nn.png","ydd3g.png", "268g2.png", "36nx4.png", "3bnyf.png", "5p8fm.png", "8y6b3.png", "mnef5.png", "yxd7m.png",] | |
) | |
iface.launch() | |