Document Question Answering
Transformers
PyTorch
English
document-processing
ocr
ner
text-classification
information-extraction
invoice
receipt
form
Instructions to use mrrobot2610/IDP-Machine-learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrrobot2610/IDP-Machine-learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="mrrobot2610/IDP-Machine-learning")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrrobot2610/IDP-Machine-learning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,586 Bytes
1a7ee60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | #!/usr/bin/env python3
"""
Test EasyOCR initialization and functionality
"""
# Fix for TensorFlow mutex warnings on macOS
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import warnings
warnings.filterwarnings('ignore')
import sys
import numpy as np
import cv2
print("Testing EasyOCR integration...")
print("=" * 60)
try:
from ocr_engine import LightweightOCR
print("\n1. Importing LightweightOCR... ✓")
print("\n2. Initializing EasyOCR engine...")
ocr = LightweightOCR(lang='en', use_gpu=False)
print(" ✓ EasyOCR initialized successfully!")
print("\n3. Creating dummy image for testing...")
# Create a white image with black text "Hello World"
image = np.ones((100, 300, 3), dtype=np.uint8) * 255
cv2.putText(image, 'Hello World', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
print(" ✓ Dummy image created")
print("\n4. Running OCR extraction...")
result = ocr.extract_text(image)
print(f" ✓ Extraction complete!")
print(f" Extracted text: '{result['text']}'")
if "Hello" in result['text'] or "World" in result['text']:
print(" ✓ Text verification passed!")
else:
print(" ⚠ Text verification warning: Expected 'Hello World', got something else")
print("\n" + "=" * 60)
print("✅ ALL TESTS PASSED!")
print("=" * 60)
except Exception as e:
print(f"\n❌ ERROR: {str(e)}")
import traceback
traceback.print_exc()
sys.exit(1)
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