TextCanvasBench: A Comprehensive Benchmark for Complex and Long-Text Image Generation

Recent open- and closed-source text-to-image (T2I) models can generate text-rich images, including scientific figures and presentation slides, yet benchmark evaluation has not kept pace with this capability. Existing text-rendering benchmarks typically focus on short text, a small number of text regions, and image-level or OCR-based aggregate metrics, limiting their ability to assess long-form content, multi-region layouts, and block-level stylistic and semantic relations in practical T2I scenarios. To address these gaps, we introduce TextCanvasBench, a benchmark of 1,600 specification-guided samples spanning 8 domains and 38 subcategories of real-world text-rich visuals, from commercial posters and academic slides to digital interface panels. The required text ranges from short labels to more than 1,000 characters, and the annotations identify text blocks and their roles. We evaluate generation through four complementary dimensions, moving from the whole image to individual text blocks and regions. At the macro image level, we assess glyph quality, the completeness and accuracy of the required text, and whether the image conveys the prompt's overall meaning. At the micro block and region level, we assess position, orientation, arrangement and reading order, font, size, and color, as well as the facts and associations tied to individual blocks. In our experiments, we evaluate nine state-of-the-art open- and closed-source T2I models (e.g., Boogu-Image and GPT-Image-2). The results show that TextCanvasBench covers a wider range of practical text-rich scenarios and reveals differences in long-text rendering capabilities that existing benchmark scores can obscure. Its detailed scores distinguish models with similar scores on other benchmarks, while its rankings agree more closely with human judgments than those of most benchmarks in our comparison.