---
title: RenomeeAI-Image-Rename-data
canonical_url: "https://www.modelscope.cn/datasets/hetianhe/RenomeeAI-Image-Rename-data"
md_url: "https://www.modelscope.cn/datasets/hetianhe/RenomeeAI-Image-Rename-data.md"
repository: hetianhe/RenomeeAI-Image-Rename-data
last_updated: 2026-10-28
license: Apache-2.0
storage_size: "3.8 MB"
downloads: 127
stars: 0
---

# RenomeeAI-Image-Rename-data

> RenomeeAI-Image-Rename-data - hetianhe 在 ModelScope 开源的数据集。测试重命名能力数据集

hetianhe/RenomeeAI-Image-Rename-data 是 ModelScope 魔搭社区上的数据集，存储大小 3.8 MB，采用 Apache-2.0 许可。

- **Repository**: hetianhe/RenomeeAI-Image-Rename-data
- **License**: Apache-2.0
- **Storage size**: 3.8 MB
- **Downloads**: 127
- **Stars**: 0
- **Last updated**: 2026-10-28

Source: https://www.modelscope.cn/datasets/hetianhe/RenomeeAI-Image-Rename-data

---

license: Apache-2.0
language:
  - en
  - zh
domain:
  - cv
task:
  - image-text-recognition
tags:
  - OCR
  - document-understanding
  - image-to-text
  - file-renaming
  - scanned-documents
  - document-processing
  - evaluation
---

# RenomeeAI OCR Image Renaming Test Collection

A small collection of challenging scanned images for testing
OCR-based document understanding and intelligent file renaming.

## Dataset Description

This dataset contains several dozen scanned images used to test
OCR-based intelligent file renaming workflows.

The goal is to evaluate whether AI systems can recognize text
in scanned images, extract meaningful information, and generate
descriptive filenames based on the image content.

## Intended Use

- OCR text recognition
- Document content understanding
- AI-based filename generation
- Scanned image processing
- File organization automation

## Example Workflow

1. Load a scanned image.
2. Extract visible text using OCR.
3. Identify useful document information.
4. Generate a descriptive filename.
5. Preserve the original file extension.

## Limitations

This is a small-scale test collection, not a standardized benchmark.
Human-verified reference filenames and benchmark scores are not
currently provided.

## Related Project

RenomeeAI is an AI-powered batch file renaming tool for
Windows and macOS.

Website: https://renomeeai.com

## 中文说明

RenomeeAI OCR 图片智能重命名测试集包含数十张扫描图片，
用于测试 OCR 文字识别、文档内容理解以及 AI 智能文件命名。

测试目标是让 AI 根据图片中的文字和文档信息，
自动生成有意义的文件名，减少人工逐个查看和重命名的工作。


# RenomeeAI OCR Image Renaming Test Collection

A small collection of challenging scanned images for evaluating OCR-based document understanding and intelligent file naming.

**Dataset Repository:** https://modelscope.cn/datasets/hetianhe/RenomeeAI-Image-Rename-data

**Related Project:** [RenomeeAI](https://renomeeai.com)

## Dataset Description

The RenomeeAI OCR Image Renaming Test Collection contains several dozen images collected for testing OCR-based file renaming workflows.

The collection focuses on a practical problem: many scanned documents and images have generic filenames such as `scan001.jpg`, `IMG_0001.png`, or `document.pdf`. These filenames do not describe the actual content, making files difficult to search, organize, and manage.

OCR and document understanding models can help extract meaningful information from these files and generate descriptive filenames automatically.

This collection is intended to support exploratory testing of that workflow.

## Intended Use

The images can be used to explore and evaluate:

- Optical character recognition on scanned images.
- Extraction of meaningful document information.
- Identification of relevant titles, dates, and keywords.
- Generation of descriptive filenames based on image content.
- Robustness of OCR-based renaming workflows when processing challenging inputs.

Potential users include OCR developers, document AI researchers, file management tool developers, and individuals experimenting with automated document organization.

## Example Workflow

A typical OCR-based renaming workflow consists of four steps:

1. **Image input:** Load an image with an existing filename.
2. **OCR processing:** Extract visible text from the image.
3. **Content understanding:** Identify the most useful information for naming the file.
4. **Filename generation:** Produce a concise and meaningful filename while preserving the original file extension.

For example, a scanned document with a generic filename might be renamed based on its detected title, document type, or date.

The final filename depends on the actual image content and the naming rules used by the application.

## Dataset Contents

The current collection contains several dozen images selected for OCR-based file renaming tests.

The primary dataset assets are the image files themselves.

The collection is small and intended for exploratory evaluation rather than large-scale model training.

At present, this README does not claim that the dataset includes human-verified OCR transcripts, standardized reference filenames, or benchmark scores.

## Suggested Evaluation Criteria

Developers using this collection may consider the following evaluation dimensions:

| Criterion | Description |
|---|---|
| OCR accuracy | Whether important visible text is recognized correctly |
| Information extraction | Whether useful titles, dates, or document identifiers are identified |
| Filename relevance | Whether the generated filename accurately reflects the document |
| Readability | Whether the filename is concise and understandable |
| Filename safety | Whether invalid filesystem characters are avoided |
| Consistency | Whether similar documents are named using consistent conventions |

These are suggested evaluation criteria, not published benchmark results.

## Relationship to RenomeeAI

This collection is associated with the development and testing of [RenomeeAI](https://renomeeai.com), an AI-powered batch file renaming tool for Windows and macOS.

RenomeeAI supports content-aware file naming workflows using document text, OCR, metadata, and natural-language naming instructions.

The dataset is published as a separate resource for exploring OCR-based document renaming scenarios. It is not required to use the RenomeeAI application.

## Limitations

- The dataset contains only a small number of images.
- It is not intended to represent all document types, languages, or scanning conditions.
- OCR quality may vary depending on image quality and model capabilities.
- Generated filenames may be subjective, and multiple filenames can be equally appropriate.
- No standardized benchmark performance claims are provided.

## Data Provenance and Licensing

The dataset publisher should verify the provenance and redistribution rights of all included images before making the collection publicly available.

Images containing personal information, confidential documents, or copyrighted third-party material should not be redistributed without appropriate authorization.

A dataset license should be specified separately once the redistribution rights have been verified.

## 中文说明

**RenomeeAI OCR 图片智能重命名测试集**是一个包含数十张扫描图片的小规模测试集合，用于探索 OCR 文字识别与 AI 文件智能命名技术。

传统文件管理中，大量扫描件和图片使用 `IMG_001.jpg`、`scan_002.png` 等无意义的文件名，用户往往需要逐个打开文件，查看内容后手动重命名。

本数据集用于测试一种更自动化的处理方式：

1. 读取扫描图片。
2. 使用 OCR 识别图片中的文字。
3. 提取标题、日期、文档类型等关键信息。
4. 根据实际内容生成具有描述性的文件名。

数据集主要面向 OCR、文档理解、智能文件管理等应用场景。

目前该集合属于小规模测试资源，尚不宣称具备完整的标准标注和正式基准测试结果。

**项目官网：** https://renomeeai.com

**数据集地址：** https://modelscope.cn/datasets/hetianhe/RenomeeAI-Image-Rename-data

#### 下载方法 
:modelscope-code[]{type="sdk"}
:modelscope-code[]{type="git"}