# Seeing The Words: Evaluating AI-generated Biblical Art

arXiv: https://arxiv.org/abs/2504.16974 · HTML: https://mlsanity.com/paper/2504.16974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015)
- [From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction](https://mlsanity.com/paper/1604.03489/) (2016)

## Built on by (most cited first, 2 of 2)

- [Mimesis, Poiesis, and Imagination: Exploring Text-to-Image Generation of Biblical Narratives](https://mlsanity.com/paper/2507.02973/) (2025)
- [Generative AI Alignment with Hinduism's Theological Plurality and Sacred Representation](https://mlsanity.com/paper/2608.28228/) (2026)

## Abstract (arXiv)

The past years witnessed a significant amount of Artificial Intelligence (AI) tools that can generate images from texts. This triggers the discussion of whether AI can generate accurate images using text from the Bible with respect to the corresponding biblical contexts and backgrounds. Despite some existing attempts at a small scale, little work has been done to systematically evaluate these generated images. In this work, we provide a large dataset of over 7K images using biblical text as prompts. These images were evaluated with multiple neural network-based tools on various aspects. We provide an assessment of accuracy and some analysis from the perspective of religion and aesthetics. Finally, we discuss the use of the generated images and reflect on the performance of the AI generators.