feat: add core OCR API integration (refs #65)

OCR Service (Python/FastAPI):
- POST /extract for synchronous OCR extraction
- POST /jobs and GET /jobs/{job_id} for async processing
- Image preprocessing (deskew, denoise) for accuracy
- HEIC conversion via pillow-heif
- Redis job queue for async processing

Backend (Fastify):
- POST /api/ocr/extract - authenticated proxy to OCR
- POST /api/ocr/jobs - async job submission
- GET /api/ocr/jobs/:jobId - job polling
- Multipart file upload handling
- JWT authentication required

File size limits: 10MB sync, 200MB async
Processing time target: <3 seconds for typical photos

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

ocr/app/models/__init__.py

@@ -0,0 +1,18 @@
+"""Pydantic models for OCR service."""
+from .schemas import (
+    DocumentType,
+    ExtractedField,
+    JobResponse,
+    JobStatus,
+    JobSubmitRequest,
+    OcrResponse,
+)
+
+__all__ = [
+    "DocumentType",
+    "ExtractedField",
+    "JobResponse",
+    "JobStatus",
+    "JobSubmitRequest",
+    "OcrResponse",
+]

ocr/app/models/schemas.py

@@ -0,0 +1,65 @@
+"""Pydantic models for OCR API request/response validation."""
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class DocumentType(str, Enum):
+    """Types of documents that can be processed."""
+
+    VIN = "vin"
+    RECEIPT = "receipt"
+    MANUAL = "manual"
+    UNKNOWN = "unknown"
+
+
+class ExtractedField(BaseModel):
+    """A single extracted field with confidence score."""
+
+    value: str
+    confidence: float = Field(ge=0.0, le=1.0)
+
+
+class OcrResponse(BaseModel):
+    """Response from OCR extraction."""
+
+    success: bool
+    document_type: DocumentType = Field(alias="documentType")
+    raw_text: str = Field(alias="rawText")
+    confidence: float = Field(ge=0.0, le=1.0)
+    extracted_fields: dict[str, ExtractedField] = Field(
+        default_factory=dict, alias="extractedFields"
+    )
+    processing_time_ms: int = Field(alias="processingTimeMs")
+
+    model_config = {"populate_by_name": True}
+
+
+class JobStatus(str, Enum):
+    """Status of an async OCR job."""
+
+    PENDING = "pending"
+    PROCESSING = "processing"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class JobResponse(BaseModel):
+    """Response for async job status."""
+
+    job_id: str = Field(alias="jobId")
+    status: JobStatus
+    progress: Optional[int] = Field(default=None, ge=0, le=100)
+    result: Optional[OcrResponse] = None
+    error: Optional[str] = None
+
+    model_config = {"populate_by_name": True}
+
+
+class JobSubmitRequest(BaseModel):
+    """Request to submit an async OCR job."""
+
+    callback_url: Optional[str] = Field(default=None, alias="callbackUrl")
+
+    model_config = {"populate_by_name": True}