Multi-Process Parallelism — Concurrency Models

PaddleOCR is a comprehensive optical character recognition framework designed for detecting and transcribing text from images and documents into structured, machine-readable formats. It provides a modular computer vision pipeline that decouples image preprocessing, text detection, and character recognition into independent, configurable stages. This architecture supports automated document digitization and multilingual text recognition, capable of identifying text in over one hundred languages across diverse environments ranging from scanned documents to industrial scenes. The framework distinguishes itself through a hardware-agnostic inference layer and a high-performance execution engine that enables consistent model deployment across CPUs, GPUs, and mobile hardware. It facilitates high-throughput production environments by utilizing static graph execution and distributed device orchestration, which allow for the scaling of recognition tasks across multiple hardware accelerators and network services. To support flexible integration, the system includes a cross-platform deployment toolkit and utilities for exporting models into universal formats. It provides granular control over resource utilization through multi-process parallelism and custom inference distribution, ensuring efficient performance for both local processing and remote network service deployment.