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Implement receipt-specific OCR extraction for fuel receipts: - Pattern matching modules for date, currency, and fuel data extraction - Receipt-optimized image preprocessing for thermal receipts - POST /extract/receipt endpoint with field extraction - Confidence scoring per extracted field - Cross-validation of fuel receipt data - Unit tests for all pattern matchers Extracted fields: merchantName, transactionDate, totalAmount, fuelQuantity, pricePerUnit, fuelGrade Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
118 lines
3.0 KiB
Python
118 lines
3.0 KiB
Python
"""Pydantic models for OCR API request/response validation."""
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from enum import Enum
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from typing import Optional
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from pydantic import BaseModel, Field
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class DocumentType(str, Enum):
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"""Types of documents that can be processed."""
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VIN = "vin"
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RECEIPT = "receipt"
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MANUAL = "manual"
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UNKNOWN = "unknown"
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class ExtractedField(BaseModel):
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"""A single extracted field with confidence score."""
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value: str
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confidence: float = Field(ge=0.0, le=1.0)
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class BoundingBox(BaseModel):
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"""Bounding box for detected region."""
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x: int
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y: int
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width: int
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height: int
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class VinAlternative(BaseModel):
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"""Alternative VIN candidate."""
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vin: str
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confidence: float = Field(ge=0.0, le=1.0)
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class VinExtractionResponse(BaseModel):
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"""Response from VIN extraction endpoint."""
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success: bool
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vin: Optional[str] = None
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confidence: float = Field(ge=0.0, le=1.0)
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bounding_box: Optional[BoundingBox] = Field(default=None, alias="boundingBox")
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alternatives: list[VinAlternative] = Field(default_factory=list)
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processing_time_ms: int = Field(alias="processingTimeMs")
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error: Optional[str] = None
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model_config = {"populate_by_name": True}
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class OcrResponse(BaseModel):
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"""Response from OCR extraction."""
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success: bool
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document_type: DocumentType = Field(alias="documentType")
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raw_text: str = Field(alias="rawText")
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confidence: float = Field(ge=0.0, le=1.0)
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extracted_fields: dict[str, ExtractedField] = Field(
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default_factory=dict, alias="extractedFields"
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)
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processing_time_ms: int = Field(alias="processingTimeMs")
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model_config = {"populate_by_name": True}
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class JobStatus(str, Enum):
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"""Status of an async OCR job."""
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PENDING = "pending"
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PROCESSING = "processing"
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COMPLETED = "completed"
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FAILED = "failed"
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class JobResponse(BaseModel):
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"""Response for async job status."""
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job_id: str = Field(alias="jobId")
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status: JobStatus
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progress: Optional[int] = Field(default=None, ge=0, le=100)
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result: Optional[OcrResponse] = None
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error: Optional[str] = None
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model_config = {"populate_by_name": True}
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class JobSubmitRequest(BaseModel):
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"""Request to submit an async OCR job."""
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callback_url: Optional[str] = Field(default=None, alias="callbackUrl")
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model_config = {"populate_by_name": True}
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class ReceiptExtractedField(BaseModel):
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"""A single extracted field from a receipt with confidence."""
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value: str | float
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confidence: float = Field(ge=0.0, le=1.0)
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class ReceiptExtractionResponse(BaseModel):
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"""Response from receipt extraction endpoint."""
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success: bool
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receipt_type: str = Field(alias="receiptType")
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extracted_fields: dict[str, ReceiptExtractedField] = Field(
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default_factory=dict, alias="extractedFields"
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)
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raw_text: str = Field(alias="rawText")
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processing_time_ms: int = Field(alias="processingTimeMs")
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error: Optional[str] = None
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model_config = {"populate_by_name": True}
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