Add OCR extraction app using TrOCR

app.py

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+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from pdf2image import convert_from_path
+import pytesseract
+
+# Load TrOCR Model from Hugging Face
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    images = convert_from_path(pdf_path)
+    extracted_text = []
+    
+    for img in images:
+        # Convert image to text using TrOCR
+        pixel_values = processor(images=img, return_tensors="pt").pixel_values
+        generated_ids = model.generate(pixel_values)
+        text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        
+        # Fallback to Tesseract if TrOCR fails
+        if not text.strip():
+            text = pytesseract.image_to_string(img)
+        
+        extracted_text.append(text)
+    
+    return "\n".join(extracted_text)
+
+# Gradio Interface
+def ocr_pipeline(pdf_file):
+    pdf_path = pdf_file.name
+    extracted_text = extract_text_from_pdf(pdf_path)
+    return extracted_text
+
+iface = gr.Interface(
+    fn=ocr_pipeline,
+    inputs=gr.File(label="Upload PDF"),
+    outputs="text",
+    title="PDF Text Extraction using TrOCR"
+)
+
+# Run the Gradio App
+if __name__ == "__main__":
+    iface.launch()
+