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import torch
from transformers import LayoutLMv2Processor, LayoutLMv2ForSequenceClassification
from PIL import Image
import numpy as np
import pytesseract
# Initialize the model and processor with caching
processor = None
model = None
def get_document_ai_models():
"""Get or initialize document AI models with proper caching."""
global processor, model
if processor is None:
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
if model is None:
model = LayoutLMv2ForSequenceClassification.from_pretrained("microsoft/layoutlmv2-base-uncased")
return processor, model
def extract_text_with_tesseract(image):
"""Extract text using Tesseract OCR."""
if isinstance(image, np.ndarray):
pil_image = Image.fromarray(image).convert("RGB")
else:
pil_image = image.convert("RGB")
# Use pytesseract for OCR
text = pytesseract.image_to_string(pil_image)
# Get word boxes for structure
boxes = pytesseract.image_to_data(pil_image, output_type=pytesseract.Output.DICT)
# Extract words and their positions
words = []
word_boxes = []
for i in range(len(boxes['text'])):
if boxes['text'][i].strip() != '':
words.append(boxes['text'][i])
x, y, w, h = boxes['left'][i], boxes['top'][i], boxes['width'][i], boxes['height'][i]
word_boxes.append([x, y, x + w, y + h])
return words, word_boxes
def extract_text_and_layout(image):
"""
Extract text and layout information using OCR and LayoutLMv2.
Args:
image: PIL Image object
Returns:
Dictionary with extracted text and layout information
"""
# Convert numpy array to PIL Image if needed
if isinstance(image, np.ndarray):
image = Image.fromarray(image).convert("RGB")
# Extract text using Tesseract
words, boxes = extract_text_with_tesseract(image)
# If no words were found, return empty result
if not words:
return {
'words': [],
'boxes': [],
'success': False
}
return {
'words': words,
'boxes': boxes,
'success': True
}
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