Performance ceiling of purely end-to-end OCR architectures

Ascertain the ultimate performance ceiling of purely end-to-end optical character recognition architectures relative to heavily optimized two-stage pipeline OCR systems by determining whether end-to-end models can fully match or surpass pipeline approaches under variations in architecture, training strategies, and data scaling laws.

Background

Qianfan-OCR is presented as a unified end-to-end model that competes strongly with pipeline systems on several benchmarks, narrowing but not entirely eliminating gaps with top pipeline methods. This raises a broader question about the fundamental limits of end-to-end designs.

The authors explicitly identify uncertainty about whether purely end-to-end architectures can ultimately match or surpass heavily engineered pipelines, calling for systematic exploration of architectural innovations, training strategies, and data scaling laws to resolve this question.