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"""
PaddleOCR Engine
High-accuracy OCR using PaddleOCR.
Supports detection, recognition, and angle classification.
"""
import time
from typing import List, Optional, Tuple
import numpy as np
from loguru import logger
from .base import OCREngine, OCRConfig, OCRResult
from ..schemas.core import BoundingBox, OCRRegion
# Try to import PaddleOCR
try:
from paddleocr import PaddleOCR
HAS_PADDLEOCR = True
except ImportError:
HAS_PADDLEOCR = False
logger.warning(
"PaddleOCR not installed. Install with: "
"pip install paddleocr paddlepaddle-gpu (or paddlepaddle for CPU)"
)
class PaddleOCREngine(OCREngine):
"""
OCR engine using PaddleOCR.
Features:
- High accuracy text detection and recognition
- Multi-language support
- GPU acceleration
- Angle classification for rotated text
"""
# Language code mapping (PaddleOCR uses different codes)
LANGUAGE_MAP = {
"en": "en",
"ch": "ch",
"chinese_cht": "chinese_cht",
"fr": "french",
"german": "german",
"es": "es",
"it": "it",
"pt": "pt",
"ru": "ru",
"japan": "japan",
"korean": "korean",
"ar": "ar",
"hi": "hi",
"latin": "latin",
}
def __init__(self, config: Optional[OCRConfig] = None):
"""Initialize PaddleOCR engine."""
super().__init__(config)
self._ocr: Optional[PaddleOCR] = None
def initialize(self):
"""Initialize PaddleOCR model."""
if not HAS_PADDLEOCR:
raise RuntimeError(
"PaddleOCR not installed. Install with: "
"pip install paddleocr paddlepaddle-gpu"
)
if self._initialized:
return
logger.info("Initializing PaddleOCR engine...")
# Map language codes
lang = self.config.languages[0] if self.config.languages else "en"
paddle_lang = self.LANGUAGE_MAP.get(lang, "en")
try:
self._ocr = PaddleOCR(
use_angle_cls=self.config.use_angle_cls,
lang=paddle_lang,
use_gpu=self.config.use_gpu,
gpu_mem=500, # GPU memory limit in MB
det_db_thresh=self.config.det_db_thresh,
det_db_box_thresh=self.config.det_db_box_thresh,
rec_batch_num=self.config.rec_batch_num,
drop_score=self.config.drop_score,
show_log=False, # Suppress verbose logging
)
self._initialized = True
logger.info(f"PaddleOCR initialized (lang={paddle_lang}, gpu={self.config.use_gpu})")
except Exception as e:
logger.error(f"Failed to initialize PaddleOCR: {e}")
raise
def recognize(
self,
image: np.ndarray,
page_number: int = 0,
) -> OCRResult:
"""
Perform OCR on an image using PaddleOCR.
Args:
image: Image as numpy array (RGB, HWC format)
page_number: Page number for multi-page documents
Returns:
OCRResult with recognized text and regions
"""
if not self._initialized:
self.initialize()
start_time = time.time()
try:
# Run OCR
results = self._ocr.ocr(image, cls=self.config.use_angle_cls)
# Process results
regions = []
all_texts = []
total_confidence = 0.0
# Results format: [[[box], (text, confidence)], ...]
if results and results[0]:
for idx, line in enumerate(results[0]):
if line is None:
continue
box_points = line[0] # [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
text, confidence = line[1]
# Skip low confidence results
if confidence < self.config.min_confidence:
continue
# Convert polygon to bounding box
bbox = self._polygon_to_bbox(box_points, image.shape[:2])
# Create polygon points
polygon = [(float(p[0]), float(p[1])) for p in box_points]
region = OCRRegion(
text=text,
confidence=float(confidence),
bbox=bbox,
polygon=polygon,
page=page_number,
line_id=idx,
engine="paddleocr",
)
regions.append(region)
all_texts.append(text)
total_confidence += confidence
processing_time = (time.time() - start_time) * 1000
return OCRResult(
regions=regions,
full_text="\n".join(all_texts),
confidence_avg=total_confidence / len(regions) if regions else 0.0,
processing_time_ms=processing_time,
engine="paddleocr",
success=True,
)
except Exception as e:
logger.error(f"PaddleOCR recognition failed: {e}")
return OCRResult(
regions=[],
full_text="",
confidence_avg=0.0,
processing_time_ms=(time.time() - start_time) * 1000,
engine="paddleocr",
success=False,
error=str(e),
)
def _polygon_to_bbox(
self,
points: List[List[float]],
image_shape: Tuple[int, int],
) -> BoundingBox:
"""Convert polygon points to bounding box."""
x_coords = [p[0] for p in points]
y_coords = [p[1] for p in points]
height, width = image_shape
return BoundingBox(
x_min=max(0, min(x_coords)),
y_min=max(0, min(y_coords)),
x_max=min(width, max(x_coords)),
y_max=min(height, max(y_coords)),
normalized=False,
page_width=width,
page_height=height,
)
def get_supported_languages(self) -> List[str]:
"""Return list of supported language codes."""
return list(self.LANGUAGE_MAP.keys())
def recognize_with_structure(
self,
image: np.ndarray,
page_number: int = 0,
) -> Tuple[OCRResult, Optional[dict]]:
"""
Perform OCR with structure analysis (tables, layout).
Args:
image: Image as numpy array
page_number: Page number
Returns:
Tuple of (OCRResult, structure_info)
"""
# First do regular OCR
ocr_result = self.recognize(image, page_number)
# PaddleOCR can also do table structure recognition
# This would require ppstructure which we can add later
structure_info = None
return ocr_result, structure_info
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