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import gradio as gr
import tempfile, os
from pdf2image import convert_from_path
import pytesseract, pdfplumber, camelot
from PIL import Image, ImageOps
import ocrmypdf
import subprocess
def extract_text_from_pdf(file):
extracted = []
pdf_path = file.name
# Create temporary paths for OCR'd PDF and text output
temp_dir = tempfile.gettempdir()
ocr_pdf_path = os.path.join(temp_dir, "ocr_searchable.pdf")
output_txt_path = os.path.join(temp_dir, "extracted_text.txt")
try:
# Step 1: Use OCRmyPDF to create a searchable PDF
print("Processing PDF with OCRmyPDF...")
ocrmypdf.ocr(
pdf_path,
ocr_pdf_path,
deskew=True,
clean=True,
force_ocr=False, # Only OCR if needed
skip_text=False,
optimize=1
)
# Step 2: Extract text from the OCR'd searchable PDF using pdfplumber
print("Extracting text from OCR'd PDF...")
with pdfplumber.open(ocr_pdf_path) as pdf:
for page_num, page in enumerate(pdf.pages):
text = page.extract_text(layout=True)
if text:
extracted.append(f"--- Page {page_num + 1} ---\n{text}")
# Extract tables if any
tables = page.extract_tables()
for table_num, table in enumerate(tables):
if table:
table_text = f"TABLE {table_num + 1} (Page {page_num + 1}):\n"
table_text += "\n".join([", ".join([str(cell) if cell else "" for cell in row]) for row in table])
extracted.append(table_text)
# Step 3: Try Camelot for additional table extraction
try:
tables = camelot.read_pdf(ocr_pdf_path, pages="all", flavor="lattice")
for i, table in enumerate(tables):
extracted.append(f"CAMELOT TABLE {i + 1}:\n{table.df.to_csv(index=False)}")
except Exception as e:
print(f"Camelot extraction failed: {e}")
# Combine all extracted text
combined_text = "\n\n".join(extracted).strip()
# If still no text, fallback to direct OCR
if len(combined_text) < 50:
print("Fallback to direct OCR...")
images = convert_from_path(pdf_path, dpi=300)
ocr_text = []
for i, img in enumerate(images):
img = img.convert("L")
img = ImageOps.invert(img)
page_text = pytesseract.image_to_string(img, config="--psm 6")
if page_text.strip():
ocr_text.append(f"--- Page {i + 1} (Direct OCR) ---\n{page_text}")
combined_text = "\n\n".join(ocr_text)
# Save the extracted text
with open(output_txt_path, "w", encoding="utf-8") as f:
f.write(combined_text)
return combined_text, output_txt_path, ocr_pdf_path
except Exception as e:
error_msg = f"Error processing PDF: {str(e)}\n\nFalling back to original extraction methods..."
print(error_msg)
# Fallback to original method if OCRmyPDF fails
try:
with pdfplumber.open(pdf_path) as pdf:
for page in pdf.pages:
text = page.extract_text(layout=True)
if text:
extracted.append(text)
tables = page.extract_tables()
for table in tables:
extracted.append("TABLE:\n" + "\n".join([", ".join(row) for row in table]))
except Exception as e2:
print("pdfplumber error:", e2)
# OCR fallback if text is too short
combined = "\n".join(extracted).strip()
if len(combined) < 100:
images = convert_from_path(pdf_path, dpi=300)
for img in images:
img = img.convert("L")
img = ImageOps.invert(img)
combined += pytesseract.image_to_string(img, config="--psm 6") + "\n"
# Save fallback output
with open(output_txt_path, "w", encoding="utf-8") as f:
f.write(combined)
return combined, output_txt_path, pdf_path # Return original PDF if OCR failed
# Create Gradio interface
app = gr.Interface(
fn=extract_text_from_pdf,
inputs=gr.File(label="📤 Upload PDF", file_types=[".pdf"]),
outputs=[
gr.Textbox(label="📄 Extracted Text", lines=25, show_copy_button=True),
gr.File(label="📥 Download Extracted Text (.txt)"),
gr.File(label="📥 Download OCR'd Searchable PDF")
],
title="Advanced PDF OCR Extractor with OCRmyPDF",
description="Upload a PDF to get: 1) Extracted text displayed and downloadable as .txt, 2) OCR'd searchable PDF download. Uses OCRmyPDF for superior OCR quality.",
allow_flagging="never",
)
if __name__ == "__main__":
app.launch() |