Cloud OCR System

The problem shaped the architecture.

Document pipelines need to accept inconsistent PDFs, photographs and scans while giving downstream systems predictable text and metadata. A useful OCR service must make slow jobs, uncertain recognition and malformed inputs manageable through the API.

Documents that look similar to a person can require very different processing: a born-digital PDF may already contain a text layer, while a rotated phone photograph needs normalization before recognition. The service therefore had to expose one stable contract while preserving enough state and confidence information to handle imperfect inputs honestly.

From product decision to working system.

I built an API-first extraction service with asynchronous processing, persistent job state and document-specific handling for scanned inputs. The system gave downstream consumers one consistent way to submit documents, follow long-running jobs and retrieve extracted text and metadata.

After ingestion, the pipeline validates and classifies the document, prepares pages for recognition and dispatches the job to asynchronous workers. Results are normalized into page-level text and metadata, persisted, and exposed through a job resource whose status distinguishes queued, processing, partially completed, completed and failed work.

The decisions that hold the system together.

1. 01

   Asynchronous job contract

   Large documents do not hold an HTTP request open; clients poll or subscribe to explicit job states.

2. 02

   Document-aware preprocessing

   PDF text layers, scans and photographs take different preparation paths before recognition.

3. 03

   Keep uncertainty visible

   Page metadata and confidence support targeted review instead of presenting every character as equally reliable.

From architecture to a verifiable result.

The delivered service turned heterogeneous documents into a consistent extraction workflow that other systems could consume asynchronously, while preserving enough processing context for review and troubleshooting.

The useful part is what the work taught me.

OCR quality depends as much on preprocessing, page classification and failure reporting as it does on the recognition engine.