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 🤖🦹🏻", examples = ["dd764.png","3p4nn.png","ydd3g.png", "268g2.png", "36nx4.png", "3bnyf.png", "5p8fm.png", "8y6b3.png", "mnef5.png", "yxd7m.png",] ) iface.launch()