AI RESEARCH
OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities
arXiv CS.CV
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ArXi:2507.15085v4 Announce Type: replace Improving visual text synthesis has long been a challenging and evolving frontier for image generation models. While recent state-of-the-art (SOTA) models have made remarkable strides in text generation capabilities, existing benchmarks inadequately assess their true performance due to narrow scope (scene text and posters only), isolated evaluation (T2I generation or editing separately), and insufficient difficulty (lacking challenging scenarios