Acquiring large-scale, well-annotated datasets is essential for training robust scene text detectors, yet the process is often resource-intensive and time-consuming. While some efforts have been made to explore the synthesis of scene text images, a notable gap remains between syn-thetic and authentic data. In this paper, we introduce a novel method that utilizes Neural Radiance Fields (NeRF) to model real-world scenes and emulate the data collection process by rendering images from diverse camera per-spectives, enriching the variability and realism of the synthesized data. A semi-supervised learning framework is proposed to categorize semantic regions within 3D scenes, ensuring consistent labeling of text regions across various viewpoints. Our method also models the pose, and view-dependent appearance of text regions, thereby offering precise control over camera poses and significantly improving the realism of text insertion and editing within scenes. Employing our technique on real-world scenes has led to the creation of a novel scene text image dataset (https://github.com/cuijl-ai/TextNeRF). Compared to other existing benchmarks, the proposed dataset is distinctive in providing not only standard annotations such as bounding boxes and transcriptions but also the information of 3D pose attributes for text regions, enabling a more detailed evaluation of the robustness of text detection algorithms. Through extensive experiments, we demonstrate the effectiveness of our proposed method in enhancing the performance of scene text detectors.