Instructions to use Kushalguptaiitb/table_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushalguptaiitb/table_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Kushalguptaiitb/table_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Kushalguptaiitb/table_test") model = AutoModelForObjectDetection.from_pretrained("Kushalguptaiitb/table_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete create_fitz_json.py
Browse files- create_fitz_json.py +0 -80
create_fitz_json.py
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import fitz # PyMuPDF
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import json
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def extract_text_to_json_with_bbox(pdf_path):
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# Open the PDF
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doc = fitz.open(pdf_path)
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pdf_name = pdf_path.split("/")[-1] # Extract the PDF filename
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result = {}
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# Iterate through each page
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for page_num in range(doc.page_count):
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page = doc.load_page(page_num)
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page_width = page.rect.width
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page_height = page.rect.height
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# Get text blocks
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blocks = page.get_text("dict")["blocks"]
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text_block_id = 0
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element_id = 0
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page_data = []
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for block in blocks:
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if block["type"] == 0: # Text block
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for line in block["lines"]:
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for span in line["spans"]:
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text = span["text"].strip()
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if text: # Only process non-empty text
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bbox = span["bbox"] # [x0, y0, x1, y1]
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font_size = span["size"]
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font_type = span["font"]
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# Create the JSON entry
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entry = {
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"text_block_id": text_block_id,
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"element_id": element_id,
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"text": text,
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"font_size": font_size,
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"font_type": font_type,
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"pdf_name": pdf_name,
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"bbox": list(bbox),
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"page_number": page_num + 1,
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"page_width": page_width,
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"page_height": page_height
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}
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page_data.append(entry)
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element_id += 1
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text_block_id += 1
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# Store page data in output dictionary
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result[str(page_num + 1)] = page_data
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# Close the PDF
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doc.close()
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return result
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def save_to_json(data, output_file):
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=4)
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if __name__ == "__main__":
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# Example usage
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# pdf_path = "/shared_disk/kushal/db_str_chunking/new_ws_structured_code/MILN/data_mike/Home Re 2021-1 Final Offering Circular.pdf"
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pdf_path = "/shared_disk/kushal/db_str_chunking/new_ws_structured_code/MILN/data_fitz_test/3_column_layout_sample.pdf"
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json_output = extract_text_to_json_with_bbox(pdf_path)
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# Save to JSON file
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with open("/shared_disk/kushal/db_str_chunking/new_ws_structured_code/MILN/output/3_column_output.json", "w") as f:
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json.dump(json_output, f, indent=4)
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# Print the JSON output
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# print(json.dumps(json_output, indent=4))
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