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OCR Invoice Processing
Invoice processing service using OCR and AWS services to extract and validate invoice information.
Problem
Invoice data arriving as scans and photographs has to become structured, trustworthy records. Manual entry is slow and error-prone, and volume arrives in unpredictable bursts that a fixed-capacity service handles badly.
Solution
A serverless pipeline triggered by document upload. Extraction runs through a managed OCR service, results are validated against expected structure, and messaging decouples each stage so bursts queue up instead of overwhelming the system.
My Contribution
- Built the serverless document processing pipeline.
- Integrated managed OCR extraction and mapped output to a structured record.
- Designed the validation layer that checks extracted fields before acceptance.
- Wired event-driven messaging between pipeline stages.
- Added failure handling so unprocessable documents are isolated, not lost.
Key Features
- Upload-triggered processing with no idle capacity
- Automated OCR extraction of invoice fields
- Structured validation of extracted values
- Event-driven fan-out between stages
- Automatic scaling with document volume
- Failure isolation and retry handling
Architecture
S3—Document uploaded to object storage.
Serverless document pipeline from upload to stored record.
Technologies
- Python
- AWS Textract
- Lambda
- SNS
- SQS
- S3
- DynamoDB
Impact
- Reduced manual invoice data entry effort
- Faster, more consistent extraction of invoice fields
- Capacity scales with volume without provisioning changes
Want to talk through the details?
Happy to go deeper on any of the engineering decisions here.