Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
smart_ocr
Extract text from images and scanned documents using PaddleOCR - supports 100+ languages
skill openclawclawhub Free
Extract text from images and scanned documents using PaddleOCR - supports 100+ languages
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Release facts
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- Tencent SkillHub
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- Version
- 1.0.0
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- duykhangdangzn1
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Documentation
Overview
This skill enables intelligent text extraction from images and scanned documents using PaddleOCR - a leading OCR engine supporting 100+ languages. Extract text from photos, screenshots, scanned PDFs, and handwritten documents with high accuracy.
How to Use
Provide the image or scanned document Optionally specify language(s) to detect I'll extract text with position and confidence data Example prompts: "Extract all text from this screenshot" "OCR this scanned PDF document" "Read the text from this business card photo" "Extract Chinese and English text from this image"
PaddleOCR Fundamentals
from paddleocr import PaddleOCR # Initialize OCR engine ocr = PaddleOCR(use_angle_cls=True, lang='en') # Run OCR on image result = ocr.ocr('image.png', cls=True) # Result structure: [[box, (text, confidence)], ...] for line in result[0]: box = line[0] # [[x1,y1], [x2,y2], [x3,y3], [x4,y4]] text = line[1][0] # Extracted text conf = line[1][1] # Confidence score print(f"{text} ({conf:.2f})")
Supported Languages
# Common language codes languages = { 'en': 'English', 'ch': 'Chinese (Simplified)', 'cht': 'Chinese (Traditional)', 'japan': 'Japanese', 'korean': 'Korean', 'french': 'French', 'german': 'German', 'spanish': 'Spanish', 'russian': 'Russian', 'arabic': 'Arabic', 'hindi': 'Hindi', 'vi': 'Vietnamese', 'th': 'Thai', # ... 100+ languages supported } # Use specific language ocr = PaddleOCR(lang='ch') # Chinese ocr = PaddleOCR(lang='japan') # Japanese ocr = PaddleOCR(lang='multilingual') # Auto-detect
Configuration Options
from paddleocr import PaddleOCR ocr = PaddleOCR( # Detection settings det_model_dir=None, # Custom detection model det_limit_side_len=960, # Max side length for detection det_db_thresh=0.3, # Binarization threshold det_db_box_thresh=0.5, # Box score threshold # Recognition settings rec_model_dir=None, # Custom recognition model rec_char_dict_path=None, # Custom character dictionary # Angle classification use_angle_cls=True, # Enable angle classification cls_model_dir=None, # Custom classification model # Language lang='en', # Language code # Performance use_gpu=True, # Use GPU if available gpu_mem=500, # GPU memory limit (MB) enable_mkldnn=True, # CPU optimization # Output show_log=False, # Suppress logs )
Processing Different Sources
Image Files # Single image result = ocr.ocr('image.png') # Multiple images images = ['img1.png', 'img2.png', 'img3.png'] for img in images: result = ocr.ocr(img) process_result(result) PDF Files (Scanned) from pdf2image import convert_from_path def ocr_pdf(pdf_path): """OCR a scanned PDF.""" # Convert PDF pages to images images = convert_from_path(pdf_path) all_text = [] for i, img in enumerate(images): # Save temp image temp_path = f'temp_page_{i}.png' img.save(temp_path) # OCR the image result = ocr.ocr(temp_path) # Extract text page_text = '\n'.join([line[1][0] for line in result[0]]) all_text.append(f"--- Page {i+1} ---\n{page_text}") os.remove(temp_path) return '\n\n'.join(all_text) URLs and Bytes import requests from io import BytesIO # From URL response = requests.get('https://example.com/image.png') result = ocr.ocr(BytesIO(response.content)) # From bytes with open('image.png', 'rb') as f: img_bytes = f.read() result = ocr.ocr(BytesIO(img_bytes))
Result Processing
def process_ocr_result(result): """Process OCR result into structured data.""" lines = [] for line in result[0]: box = line[0] text = line[1][0] confidence = line[1][1] # Calculate bounding box x_coords = [p[0] for p in box] y_coords = [p[1] for p in box] lines.append({ 'text': text, 'confidence': confidence, 'bbox': { 'left': min(x_coords), 'top': min(y_coords), 'right': max(x_coords), 'bottom': max(y_coords), }, 'raw_box': box }) return lines # Sort by position (top to bottom, left to right) def sort_by_position(lines): return sorted(lines, key=lambda x: (x['bbox']['top'], x['bbox']['left']))
Text Layout Reconstruction
def reconstruct_layout(result, line_threshold=10): """Reconstruct text layout from OCR results.""" lines = process_ocr_result(result) lines = sort_by_position(lines) # Group into logical lines text_lines = [] current_line = [] current_y = None for line in lines: y = line['bbox']['top'] if current_y is None or abs(y - current_y) < line_threshold: current_line.append(line) current_y = y else: # New line text_lines.append(' '.join([l['text'] for l in current_line])) current_line = [line] current_y = y # Add last line if current_line: text_lines.append(' '.join([l['text'] for l in current_line])) return '\n'.join(text_lines)
Best Practices
Preprocess Images: Improve quality before OCR Choose Correct Language: Specify language for better accuracy Handle Multi-column: Process columns separately Filter Low Confidence: Skip results below threshold Batch Processing: Process multiple images efficiently
Image Preprocessing
from PIL import Image, ImageEnhance, ImageFilter def preprocess_image(image_path): """Preprocess image for better OCR.""" img = Image.open(image_path) # Convert to grayscale img = img.convert('L') # Enhance contrast enhancer = ImageEnhance.Contrast(img) img = enhancer.enhance(2.0) # Sharpen img = img.filter(ImageFilter.SHARPEN) # Save preprocessed preprocessed_path = 'preprocessed.png' img.save(preprocessed_path) return preprocessed_path
Batch OCR with Progress
from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor def batch_ocr(image_paths, max_workers=4): """OCR multiple images in parallel.""" results = {} def process_single(img_path): result = ocr.ocr(img_path) return img_path, result with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(process_single, p) for p in image_paths] for future in tqdm(futures, desc="Processing OCR"): path, result = future.result() results[path] = result return results
Example 1: Business Card Reader
from paddleocr import PaddleOCR import re def read_business_card(image_path): """Extract contact info from business card.""" ocr = PaddleOCR(use_angle_cls=True, lang='en') result = ocr.ocr(image_path) # Extract all text all_text = [] for line in result[0]: all_text.append(line[1][0]) full_text = '\n'.join(all_text) # Parse contact info contact = { 'name': None, 'email': None, 'phone': None, 'company': None, 'title': None, 'raw_text': full_text } # Email pattern email_match = re.search(r'[\w\.-]+@[\w\.-]+\.\w+', full_text) if email_match: contact['email'] = email_match.group() # Phone pattern phone_match = re.search(r'[\+\d][\d\s\-\(\)]{8,}', full_text) if phone_match: contact['phone'] = phone_match.group().strip() # Name is usually the largest/first text if all_text: contact['name'] = all_text[0] return contact card_info = read_business_card('business_card.jpg') print(f"Name: {card_info['name']}") print(f"Email: {card_info['email']}") print(f"Phone: {card_info['phone']}")
Example 2: Receipt Scanner
from paddleocr import PaddleOCR import re def scan_receipt(image_path): """Extract items and total from receipt.""" ocr = PaddleOCR(use_angle_cls=True, lang='en') result = ocr.ocr(image_path) lines = [] for line in result[0]: text = line[1][0] y_pos = line[0][0][1] lines.append({'text': text, 'y': y_pos}) # Sort by vertical position lines.sort(key=lambda x: x['y']) receipt = { 'items': [], 'subtotal': None, 'tax': None, 'total': None } for line in lines: text = line['text'] # Look for total if 'total' in text.lower(): amount = re.search(r'\$?([\d,]+\.?\d*)', text) if amount: if 'sub' in text.lower(): receipt['subtotal'] = float(amount.group(1).replace(',', '')) else: receipt['total'] = float(amount.group(1).replace(',', '')) # Look for tax elif 'tax' in text.lower(): amount = re.search(r'\$?([\d,]+\.?\d*)', text) if amount: receipt['tax'] = float(amount.group(1).replace(',', '')) # Look for items (line with price) else: item_match = re.search(r'(.+?)\s+\$?([\d,]+\.?\d+)$', text) if item_match: receipt['items'].append({ 'name': item_match.group(1).strip(), 'price': float(item_match.group(2).replace(',', '')) }) return receipt receipt_data = scan_receipt('receipt.jpg') print(f"Items: {len(receipt_data['items'])}") print(f"Total: ${receipt_data['total']}")
Example 3: Multi-language Document
from paddleocr import PaddleOCR def ocr_multilingual(image_path, languages=['en', 'ch']): """OCR document with multiple languages.""" all_results = {} for lang in languages: ocr = PaddleOCR(use_angle_cls=True, lang=lang) result = ocr.ocr(image_path) texts = [] for line in result[0]: texts.append({ 'text': line[1][0], 'confidence': line[1][1] }) all_results[lang] = texts # Merge results, keeping highest confidence merged = {} for lang, texts in all_results.items(): for item in texts: text = item['text'] conf = item['confidence'] if text not in merged or merged[text]['confidence'] < conf: merged[text] = {'confidence': conf, 'language': lang} return merged result = ocr_multilingual('bilingual_document.png') for text, info in result.items(): print(f"[{info['language']}] {text} ({info['confidence']:.2f})")
Limitations
Handwritten text accuracy varies Very small text may not be detected Complex backgrounds reduce accuracy Rotated text needs angle classification GPU recommended for best performance
Installation
# CPU version pip install paddlepaddle paddleocr # GPU version (CUDA 11.x) pip install paddlepaddle-gpu paddleocr # Additional dependencies pip install pdf2image Pillow
Resources
PaddleOCR GitHub Model Zoo Multi-language Support
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Config
- SKILL.md
- _meta.json