lumimusta/Low-light_Scene_Text_Dataset

--- license: mit task_categories: - image-to-text language: - en - es pretty_name: Low-light Scene Text Dataset configs: - config_name: default data_files: - split: train path: train.jsonl - split: test path: test.jsonl - split: test_real path: test_real.jsonl --- # Low-light Scene Text Dataset This repository provides a low-light scene text recognition dataset for studying text recognition under challenging illumination conditions. The dataset is designed to support research on Low-light Scene Text Recognition (LLSTR), where text images may suffer from low contrast, noise, uneven illumination, blur, and other degradations commonly observed in nighttime or poorly lit environments. The dataset contains two main parts: - **LSTR**: a large-scale low-light scene text recognition dataset derived from well-lit scene text datasets, including ICDAR2015, IIIT5K, and WordArt. - **ESTR**: a real-world nighttime scene text evaluation set containing real low-light street-scene images with English and Spanish text. The goal of this dataset is to provide a benchmark for evaluating OCR and low-light image enhancement methods in dark environments, especially for approaches that aim to preserve text readability rather than only improve visual brightness. ## Dataset Files This repository contains the following files: | File | Description | |---|---| | `low_light_train.zip` | Low-light training images | | `well_lit_train.zip` | Corresponding well-lit training images | | `low_light_test.zip` | Low-light test images | | `well_lit_test.zip` | Corresponding well-lit test images | | `low_light_test_real.zip` | Real-world low-light test images | | `train.jsonl` | Training annotations | | `test.jsonl` | Test annotations | | `test_real.jsonl` | Real-world test annotations | | `train_label.txt` | Training labels | | `test_label.txt` | Test labels | | `low_light_test_real.txt` | Labels for the real-world low-light test set | ## Splits The dataset is organized into the following splits: | Split | Annotation File | Image Archive | |---|---|---| | `train` | `train.jsonl` | `low_light_train.zip`, `well_lit_train.zip` | | `test` | `test.jsonl` | `low_light_test.zip`, `well_lit_test.zip` | | `test_real` | `test_real.jsonl` | `low_light_test_real.zip` | ## Dataset Description Low-light scene text recognition is challenging because images captured in dark environments often contain weak contrast, noise, color distortion, and uneven illumination. These degradations can significantly reduce the performance of standard OCR models trained mainly on well-lit images. This dataset provides low-light and well-lit scene text images for training and evaluation. The low-light portion is based on scene text images from ICDAR2015, IIIT5K, and WordArt, while the real-world evaluation subset contains nighttime street-scene images collected under natural low-light conditions. The real-world subset includes diverse text appearances, backgrounds, fonts, and languages, with text instances in English and Spanish. ## Dataset Statistics | Dataset Part | Description | |---|---| | LSTR | Low-light scene text recognition data derived from well-lit scene text datasets | | ESTR | Real-world nighttime scene text images for evaluation | The dataset is intended as a compact benchmark for low-light text recognition research. It is especially useful for evaluating whether OCR systems and low-light enhancement methods can preserve fine text structures such as character strokes, edges, and boundaries. ## Usage After downloading the dataset, extract the image archives before using the annotation files. Example: ```bash unzip low_light_train.zip -d low_light_train unzip well_lit_train.zip -d well_lit_train unzip low_light_test.zip -d low_light_test unzip well_lit_test.zip -d well_lit_test unzip low_light_test_real.zip -d low_light_test_real ``` The annotation files can then be loaded from the corresponding `.jsonl` files. Example: ```python import json with open("train.jsonl", "r", encoding="utf-8") as f: samples = [json.loads(line) for line in f] print(samples[0]) ``` ## Intended Use This dataset is intended for research on: - Low-light scene text recognition - Robust OCR under nighttime or poorly illuminated conditions - Low-light image enhancement for text readability - Joint optimization of image enhancement and OCR models - Benchmarking OCR models under illumination degradation ## Citation If you use this dataset, please cite the corresponding paper: ```bibtex @inproceedings{fu2026reading, title={Reading in the Dark: Low-light Scene Text Recognition}, author={Fu, Xuanshuo and Kang, Lei and Valveny, Ernest and Karatzas, Dimosthenis and Vazquez-Corral, Javier}, booktitle={ICPR}, year={2026} } ``` ## Notes The image files are stored as ZIP archives. The Hugging Face Dataset Viewer may only display the `.jsonl` annotation files and may not preview all images inside the ZIP archives directly. Please download and extract the ZIP files for full dataset usage.--- 许可证：MIT协议 任务类别：图像到文本（image-to-text） 语言：英语、西班牙语 美观名称：低光照场景文本数据集（Low-light Scene Text Dataset） 配置项： - 配置名称：default 数据文件： - 划分集：train，路径：train.jsonl - 划分集：test，路径：test.jsonl - 划分集：test_real，路径：test_real.jsonl --- # 低光照场景文本数据集（Low-light Scene Text Dataset） 本仓库提供用于研究极端光照条件下文本识别的低光照场景文本识别数据集，旨在支撑低光照场景文本识别（Low-light Scene Text Recognition, LLSTR）相关研究。该数据集针对夜间或低光照环境中常见的低对比度、噪声、光照不均、模糊等图像退化场景下的文本图像设计。 数据集包含两大核心部分： - **LSTR**：从光照良好的场景文本数据集（包含ICDAR2015、IIIT5K与WordArt）衍生的大规模低光照场景文本识别数据集 - **ESTR**：真实夜间场景文本评估集，包含带有英语与西班牙语文本的真实低光照街景图像 本数据集的目标是为黑暗环境下的光学字符识别（Optical Character Recognition, OCR）与低光照图像增强方法提供评测基准，尤其适用于旨在保留文本可读性而非仅提升视觉亮度的相关算法。 ## 数据集文件 | 文件名 | 描述 | |---|---| | `low_light_train.zip` | 低光照训练图像 | | `well_lit_train.zip` | 对应的光照正常的训练图像 | | `low_light_test.zip` | 低光照测试图像 | | `well_lit_test.zip` | 对应的光照正常的测试图像 | | `low_light_test_real.zip` | 真实场景低光照测试图像 | | `train.jsonl` | 训练集标注 | | `test.jsonl` | 测试集标注 | | `test_real.jsonl` | 真实场景测试集标注 | | `train_label.txt` | 训练集标签 | | `test_label.txt` | 测试集标签 | | `low_light_test_real.txt` | 真实场景低光照测试集标签 | ## 数据集划分 | 划分集 | 标注文件 | 图像压缩包 | |---|---|---| | `train` | `train.jsonl` | `low_light_train.zip`、`well_lit_train.zip` | | `test` | `test.jsonl` | `low_light_test.zip`、`well_lit_test.zip` | | `test_real` | `test_real.jsonl` | `low_light_test_real.zip` | ## 数据集说明 低光照场景下的文本识别极具挑战，因为黑暗环境中采集的图像往往存在对比度弱、噪声、色彩失真与光照不均等问题。这些图像退化问题会显著降低仅在光照正常图像上训练的标准OCR模型的性能。 本数据集提供低光照与光照正常的场景文本图像用于模型训练与评测。其中低光照子集基于ICDAR2015、IIIT5K与WordArt的场景文本图像构建，而真实场景评测子集则包含自然低光照条件下采集的夜间街景图像。 真实场景子集包含多样化的文本外观、背景、字体与语言，涵盖英语与西班牙语的文本实例。 ## 数据集统计 | 数据集子集 | 描述 | |---|---| | LSTR | 从光照正常的场景文本数据集衍生的低光照场景文本识别数据 | | ESTR | 用于评测的真实夜间场景文本图像 | 本数据集旨在作为低光照文本识别研究的轻量化评测基准，尤其适用于评估OCR系统与低光照增强方法能否保留文本的精细结构，例如字符笔画、边缘与边界。 ## 使用方法 下载数据集后，请先解压图像压缩包，再使用标注文件。 示例： bash unzip low_light_train.zip -d low_light_train unzip well_lit_train.zip -d well_lit_train unzip low_light_test.zip -d low_light_test unzip well_lit_test.zip -d well_lit_test unzip low_light_test_real.zip -d low_light_test_real 随后可从对应的`.jsonl`文件加载标注。 示例： python import json with open("train.jsonl", "r", encoding="utf-8") as f: samples = [json.loads(line) for line in f] print(samples[0]) ## 预期用途 本数据集适用于以下方向的研究： - 低光照场景文本识别 - 夜间或低光照条件下的鲁棒OCR - 面向文本可读性的低光照图像增强 - 图像增强与OCR模型的联合优化 - 光照退化场景下的OCR模型评测 ## 引用 若使用本数据集，请引用如下论文： bibtex @inproceedings{fu2026reading, title={Reading in the Dark: Low-light Scene Text Recognition}, author={Fu, Xuanshuo and Kang, Lei and Valveny, Ernest and Karatzas, Dimosthenis and Vazquez-Corral, Javier}, booktitle={ICPR}, year={2026} } ## 注意事项 图像文件以ZIP压缩包形式存储。Hugging Face数据集查看器仅可显示`.jsonl`标注文件，无法直接预览ZIP压缩包内的全部图像。请下载并解压ZIP文件以完整使用本数据集。