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sample_usage.py

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+# Sample code for using the Portuguese OCR Dataset
+
+import h5py
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from transformers import VisionEncoderDecoderModel, TrOCRProcessor
+import torch
+
+# Load the dataset
+def load_dataset(file_path):
+    with h5py.File(file_path, 'r') as f:
+        images = f['images'][:]
+        texts = [t.decode('utf-8') if isinstance(t, bytes) else t for t in f['texts'][:]]
+    return images, texts
+
+# Load train dataset
+train_images, train_texts = load_dataset('train_dataset.h5')
+print(f"Loaded {len(train_images)} training samples")
+
+# Display a random sample
+def display_sample(images, texts, idx=None):
+    if idx is None:
+        idx = np.random.randint(0, len(images))
+    
+    print(f"Text: {texts[idx]}")
+    
+    plt.figure(figsize=(12, 3))
+    plt.imshow(images[idx])
+    plt.axis('off')
+    plt.title(f"Sample {idx}")
+    plt.show()
+    
+    return idx
+
+# Display a random sample
+sample_idx = display_sample(train_images, train_texts)
+
+# Example of using with TrOCR
+def test_with_trocr(image, model_name="microsoft/trocr-base-printed"):
+    # Load model and processor
+    processor = TrOCRProcessor.from_pretrained(model_name)
+    model = VisionEncoderDecoderModel.from_pretrained(model_name)
+    
+    # Convert image to PIL if it's a numpy array
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    
+    # Prepare image
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    
+    # Generate prediction
+    generated_ids = model.generate(pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return generated_text
+
+# Uncomment to test a sample with a pre-trained TrOCR model
+# sample_img = train_images[sample_idx]
+# predicted_text = test_with_trocr(sample_img)
+# print(f"Original text: {train_texts[sample_idx]}")
+# print(f"Predicted text: {predicted_text}")