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import pytesseract
from pytesseract import Output
from pdf2image import convert_from_path
from PIL import Image
import os
import logging
import numpy as np
logger = logging.getLogger("ocr_engine")
def extract_text_and_conf(file_path: str) -> tuple[str, float]:
"""
Extracts text AND confidence score from a PDF or Image.
Returns: (text_content, average_confidence_0_to_100)
"""
if not os.path.exists(file_path):
return "", 0.0
text_content = ""
confidences = []
try:
images = []
# 1. Load Images
if file_path.lower().endswith('.pdf'):
try:
images = convert_from_path(file_path)
except Exception as e:
logger.error(f"PDF Convert Error: {e}")
return "", 0.0
elif file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp')):
try:
images = [Image.open(file_path)]
except Exception as e:
logger.error(f"Image Open Error: {e}")
return "", 0.0
# 2. Process Each Page
for i, image in enumerate(images):
# A. Get Layout-Preserved Text (Best for LLM)
page_text = pytesseract.image_to_string(image)
text_content += f"--- Page {i+1} ---\n{page_text}\n"
# B. Get Confidence Data (Best for KPIs)
# data_dict keys: ['level', 'page_num', 'block_num', 'par_num', 'line_num', 'word_num', 'left', 'top', 'width', 'height', 'conf', 'text']
data = pytesseract.image_to_data(image, output_type=Output.DICT)
# Filter valid confidences (ignore -1 which usually means whitespace/block info)
for conf in data['conf']:
# Tesseract returns -1 for structural elements (not words)
if conf != -1:
confidences.append(conf)
# 3. Calculate Average Confidence
avg_conf = 0.0
if confidences:
avg_conf = sum(confidences) / len(confidences)
return text_content.strip(), round(avg_conf, 2)
except Exception as e:
logger.error(f"OCR Critical Error: {e}")
return "", 0.0 |