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"""
Base OCR Interface
Defines the abstract OCR engine interface and common data structures.
"""
from abc import ABC, abstractmethod
from typing import List, Optional, Dict, Any, Tuple
from dataclasses import dataclass, field
from enum import Enum
import numpy as np
from pydantic import BaseModel, Field
from ..schemas.core import BoundingBox, OCRRegion
class OCRLanguage(str, Enum):
"""Supported OCR languages."""
ENGLISH = "en"
CHINESE_SIMPLIFIED = "ch"
CHINESE_TRADITIONAL = "chinese_cht"
FRENCH = "fr"
GERMAN = "german"
SPANISH = "es"
ITALIAN = "it"
PORTUGUESE = "pt"
RUSSIAN = "ru"
JAPANESE = "japan"
KOREAN = "korean"
ARABIC = "ar"
HINDI = "hi"
LATIN = "latin"
class OCRConfig(BaseModel):
"""Configuration for OCR processing."""
# Engine selection
engine: str = Field(default="paddle", description="OCR engine: paddle or tesseract")
# Language settings
languages: List[str] = Field(
default=["en"],
description="Languages to detect (ISO codes)"
)
# Detection settings
det_db_thresh: float = Field(
default=0.3,
ge=0.0,
le=1.0,
description="Detection threshold for text regions"
)
det_db_box_thresh: float = Field(
default=0.5,
ge=0.0,
le=1.0,
description="Box detection threshold"
)
# Recognition settings
rec_batch_num: int = Field(
default=6,
ge=1,
description="Recognition batch size"
)
min_confidence: float = Field(
default=0.5,
ge=0.0,
le=1.0,
description="Minimum confidence threshold"
)
# Performance settings
use_gpu: bool = Field(default=True, description="Use GPU acceleration")
gpu_id: int = Field(default=0, ge=0, description="GPU device ID")
use_angle_cls: bool = Field(
default=True,
description="Use angle classification for rotated text"
)
use_dilation: bool = Field(
default=False,
description="Use dilation for detection"
)
# Output settings
drop_score: float = Field(
default=0.5,
ge=0.0,
le=1.0,
description="Drop results below this score"
)
return_word_boxes: bool = Field(
default=False,
description="Return word-level boxes (vs line-level)"
)
# Preprocessing
preprocess_resize: Optional[int] = Field(
default=None,
description="Resize image max dimension before OCR"
)
preprocess_denoise: bool = Field(
default=False,
description="Apply denoising before OCR"
)
@dataclass
class OCRResult:
"""
Result of OCR processing for a single image/page.
"""
regions: List[OCRRegion] = field(default_factory=list)
full_text: str = ""
confidence_avg: float = 0.0
processing_time_ms: float = 0.0
engine: str = "unknown"
language_detected: Optional[str] = None
# Error handling
success: bool = True
error: Optional[str] = None
def get_text_in_bbox(self, bbox: BoundingBox) -> str:
"""Get text within a bounding box."""
texts = []
for region in self.regions:
if bbox.contains(region.bbox) or bbox.iou(region.bbox) > 0.5:
texts.append(region.text)
return " ".join(texts)
def filter_by_confidence(self, min_confidence: float) -> "OCRResult":
"""Return new result with regions above confidence threshold."""
filtered_regions = [r for r in self.regions if r.confidence >= min_confidence]
return OCRResult(
regions=filtered_regions,
full_text=" ".join(r.text for r in filtered_regions),
confidence_avg=sum(r.confidence for r in filtered_regions) / len(filtered_regions) if filtered_regions else 0,
processing_time_ms=self.processing_time_ms,
engine=self.engine,
language_detected=self.language_detected,
success=self.success,
error=self.error,
)
class OCREngine(ABC):
"""
Abstract base class for OCR engines.
Defines the interface that all OCR implementations must follow.
"""
def __init__(self, config: Optional[OCRConfig] = None):
"""
Initialize OCR engine.
Args:
config: OCR configuration
"""
self.config = config or OCRConfig()
self._initialized = False
@abstractmethod
def initialize(self):
"""Initialize the OCR engine (load models, etc.)."""
pass
@abstractmethod
def recognize(
self,
image: np.ndarray,
page_number: int = 0,
) -> OCRResult:
"""
Perform OCR on an image.
Args:
image: Image as numpy array (RGB, HWC format)
page_number: Page number for multi-page documents
Returns:
OCRResult with recognized text and regions
"""
pass
def recognize_batch(
self,
images: List[np.ndarray],
page_numbers: Optional[List[int]] = None,
) -> List[OCRResult]:
"""
Perform OCR on multiple images.
Args:
images: List of images
page_numbers: Optional page numbers
Returns:
List of OCRResult
"""
if page_numbers is None:
page_numbers = list(range(len(images)))
results = []
for img, page_num in zip(images, page_numbers):
results.append(self.recognize(img, page_num))
return results
@abstractmethod
def get_supported_languages(self) -> List[str]:
"""Return list of supported language codes."""
pass
@property
def name(self) -> str:
"""Return engine name."""
return self.__class__.__name__
@property
def is_initialized(self) -> bool:
"""Check if engine is initialized."""
return self._initialized
def __enter__(self):
"""Context manager entry."""
if not self._initialized:
self.initialize()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""Context manager exit."""
pass
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