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Restructure README overview #3

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98 changes: 62 additions & 36 deletions README.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,67 @@ No required OCR stack. No document-processing service lock-in.

## Quick Start

Install doc7, start a local vision model in LM Studio or Ollama, then convert a
document:

```bash
# macOS or Linux
curl -fsSL https://raw.githubusercontent.com/magicrew/doc7/main/scripts/install.sh | bash

# Windows PowerShell
irm https://raw.githubusercontent.com/magicrew/doc7/main/scripts/install.ps1 | iex

# Convert a document
doc7 report.pdf
```

The first run discovers the local model endpoint and saves the selected model
on this machine. For a remote endpoint, configure it with `doc7 setup`.

## Understand Complex Pages

[![doc7 turns Attention Is All You Need into AI-ready Markdown](./examples/attention-is-all-you-need/showcase.webp)](./examples/attention-is-all-you-need/input.webp)

One raster-only page from *Attention Is All You Need*. No text layer. doc7
recovers the paper identity, Figure 2, the displayed equation, the scaling
rationale, the technical footnote, and the ordered relationships inside both
attention diagrams as searchable Markdown.

The same pipeline processes complete papers and multi-page reports, then
rebuilds the ordered pages into one document.

`doc7` reads the whole page instead of stopping at character extraction. Bring
any OpenAI-compatible multimodal model, including a private or local deployment.
There is no required OCR stack and no per-page document-parser fee from doc7.

## Measured on Real Inputs

[![doc7 Visual Understanding Benchmark](./assets/readme/benchmark/benchmark.webp)](./benchmarks/visual-report/README.md)

Two raster-only PDFs. Fifteen machine-checkable visual facts. MarkItDown OCR
and doc7 used the same `qwen3.5-9b` model through the same local
OpenAI-compatible endpoint. Docling used its standard local pipeline.

In this run, doc7 recovered **15/15** checked facts, compared with 9/15 for
MarkItDown with its OCR plugin and 3/15 for Docling's standard pipeline.

## One Pipeline for Every Format

[![doc7 processes every document through one visual-understanding pipeline](./assets/readme/formats/formats.webp)](#supported-inputs)

Different containers enter the same page-understanding pipeline. Text, tables,
formulas, charts, diagram relationships, image meaning, and visible UI state
leave as one searchable Markdown document.

## Built Around the CLI

[![doc7 command-line interface on macOS, Linux, and Windows](./assets/readme/cli/cli.webp)](#quick-start)

The same binary provides the interactive CLI, batch processing, model checks,
MCP, the Go SDK, and the asynchronous HTTP service.

## Detailed Setup and Usage

**Download:** [macOS, Linux, and Windows CLI archives](https://github.com/magicrew/doc7/releases)

Install the latest release without administrator privileges.
Expand Down Expand Up @@ -200,27 +261,7 @@ operate a model. doc7 is designed for the opposite case: reuse a local or
private model, eliminate a recurring document-parser bill, and turn long-term
document conversion into infrastructure you own.

## High-Complexity Papers, Precise Markdown

[![doc7 turns Attention Is All You Need into AI-ready Markdown](./examples/attention-is-all-you-need/showcase.webp)](./examples/attention-is-all-you-need/input.webp)

One raster-only page from *Attention Is All You Need*. No text layer. doc7
recovers the paper identity, Figure 2, the displayed equation, the scaling
rationale, the technical footnote, and the ordered relationships inside both
attention diagrams as searchable Markdown.

The same pipeline processes complete papers and multi-page reports, then
rebuilds the ordered pages into one document.

`doc7` reads the whole page instead of stopping at character extraction. Bring any OpenAI-compatible multimodal model, including a private or local deployment. There is no required OCR stack and no per-page document-parser fee from doc7.

## Open Benchmark

[![doc7 Visual Understanding Benchmark](./assets/readme/benchmark/benchmark.webp)](./benchmarks/visual-report/README.md)

Two raster-only PDFs. Fifteen machine-checkable visual facts. MarkItDown OCR
and doc7 used the same `qwen3.5-9b` model through the same local
OpenAI-compatible endpoint. Docling used its standard local pipeline.
## Open Benchmark Details

| System | Attention paper | Visual report | Combined | Raw Markdown |
| --- | ---: | ---: | ---: | ---: |
Expand Down Expand Up @@ -253,14 +294,6 @@ page 4 of *Attention Is All You Need* and is excluded from doc7's MIT license.

For a pinned large-scale evaluation, use the [olmOCR-Bench adapter](./benchmarks/olmocr/README.md). It supports the upstream 1,403-PDF / 7,010-fact suite without redistributing its third-party documents; doc7 does not claim a full-suite ranking until a complete pinned run is published.

## One Pipeline, Every Document

[![doc7 processes every document through one visual-understanding pipeline](./assets/readme/formats/formats.webp)](#supported-inputs)

Different containers enter the same page-understanding pipeline. Text,
tables, formulas, charts, diagram relationships, image meaning, and visible UI
state leave as one searchable Markdown document.

## A Different Architecture

| Primary approach | Representative projects | What happens | What you operate |
Expand All @@ -272,13 +305,6 @@ state leave as one searchable Markdown document.

The model remains your choice. `doc7` does not prescribe a model size or claim quality that has not been measured. With a private open model, there is no required external document-processing service and no doc7 usage quota.

## The CLI Is The Product

[![doc7 command-line interface on macOS, Linux, and Windows](./assets/readme/cli/cli.webp)](#quick-start)

The same binary provides the interactive CLI, batch processing, model checks,
MCP, the Go SDK, and the asynchronous HTTP service.

## Model, Dependency, and Recovery Workflow

Discover the model IDs exposed by an OpenAI-compatible vision endpoint:
Expand Down
88 changes: 55 additions & 33 deletions README.zh-CN.md
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Expand Up @@ -16,6 +16,60 @@ doc7 通过你自己的 OpenAI 兼容多模态模型，把 PDF、Office、扫描

## 快速开始

安装 doc7，启动 LM Studio 或 Ollama 中的本地视觉模型，然后转换文档：

```bash
# macOS 或 Linux
curl -fsSL https://raw.githubusercontent.com/magicrew/doc7/main/scripts/install.sh | bash

# Windows PowerShell
irm https://raw.githubusercontent.com/magicrew/doc7/main/scripts/install.ps1 | iex

# 转换文档
doc7 报告.pdf
```

首次运行会自动发现本地模型接口，并把选择保存到当前机器。需要使用远程接口
时，运行 `doc7 setup` 配置即可。

## 复杂页面，也能完整理解

[![doc7 将 Attention Is All You Need 转换为 AI 可用的 Markdown](./examples/attention-is-all-you-need/showcase.zh-CN.webp)](./examples/attention-is-all-you-need/input.webp)

《Attention Is All You Need》中的一页纯图片 PDF，没有文本层。doc7 把论文身份、
Figure 2、展示公式、缩放原因、技术脚注，以及两张注意力图内部的顺序和并行关系，
完整转换为可检索的 Markdown。

同一套流程也可以处理完整论文和多页报告，再按原始页序重建为一份文档。

doc7 读取整页信息，而不止字符。你可以接入任何兼容 OpenAI 接口的多模态模型，
包括本地模型和私有化部署。doc7 不要求单独搭建 OCR 技术栈，也不收取按页处理费用。

## 用真实输入衡量效果

[![doc7 视觉理解 Benchmark](./assets/readme/benchmark/benchmark.zh-CN.webp)](./benchmarks/visual-report/README.md)

两份纯图片 PDF，15 项可机器校验的视觉事实。MarkItDown OCR 与 doc7 通过同一个
本地 OpenAI 兼容接口使用同一个 `qwen3.5-9b` 模型，Docling 使用标准本地流水线。

这次运行中，doc7 恢复了 **15/15** 项事实；MarkItDown 加 OCR 为 9/15，Docling
标准流水线为 3/15。

## 一套流程处理所有格式

[![doc7 使用一套视觉理解流程处理一切文档](./assets/readme/formats/formats.zh-CN.webp)](#支持的输入)

不同格式进入同一套页面理解流程。正文、表格、公式、图表、图示关系、图像含义和
可见界面状态，最终组成一份可检索的 Markdown 文档。

## 从命令行直接开始

[![doc7 在 macOS、Linux 和 Windows 上的命令行界面](./assets/readme/cli/cli.zh-CN.webp)](#快速开始)

同一个二进制文件提供交互式 CLI、批量处理、模型检查、MCP、Go SDK 和异步 HTTP 服务。

## 详细配置与使用

**直接下载：** [macOS、Linux 和 Windows CLI 压缩包](https://github.com/magicrew/doc7/releases)

不需要管理员权限，直接安装最新发行版。
Expand Down Expand Up @@ -176,25 +230,7 @@ API 通常按页、图片或功能调用计费；本地 doc7 的主要成本是
选择：复用本地或私有模型，取消持续累加的文档解析账单，把长期文档转换能力
变成自己拥有的基础设施。

## 高复杂度论文，也能精准转换

[![doc7 将 Attention Is All You Need 转换成 AI 可用的 Markdown](./examples/attention-is-all-you-need/showcase.zh-CN.webp)](./examples/attention-is-all-you-need/input.webp)

《Attention Is All You Need》中的一页纯图片 PDF，没有文本层。doc7
把论文身份、Figure 2、展示公式、缩放原因、技术脚注，以及两张注意力图
内部的顺序和并行关系，完整转换为可检索的 Markdown。

同一套流程也可以处理完整论文和多页报告，再按原始页序重建为一份文档。

doc7 读取整页信息，而不止字符。你可以接入任何兼容 OpenAI 接口的多模态模型，包括本地模型和私有化部署。doc7 不要求单独搭建 OCR 技术栈，也不收取按页处理费用。

## 公开 Benchmark

[![doc7 视觉理解 Benchmark](./assets/readme/benchmark/benchmark.zh-CN.webp)](./benchmarks/visual-report/README.md)

两份纯图片 PDF，15 项可机器校验的视觉事实。MarkItDown OCR 与 doc7
通过同一个本地 OpenAI 兼容接口使用同一个 `qwen3.5-9b` 模型，Docling
使用标准本地流水线。
## 公开 Benchmark 详情

| 系统 | Attention 论文 | 视觉报告 | 合计 | 原始 Markdown |
| --- | ---: | ---: | ---: | ---: |
Expand Down Expand Up @@ -223,13 +259,6 @@ doc7 的 MIT 许可证范围。[查看来源和授权记录](./examples/attentio

需要大规模评估时，使用 [olmOCR-Bench 适配器](./benchmarks/olmocr/README.md)。它支持上游固定版本的 1,403 份 PDF、7,010 个机器可判定事实，不把第三方文档打包进 doc7；在完整、固定版本的运行完成前，doc7 不发布全量排名结论。

## 一套流程，处理一切文档

[![doc7 使用一套视觉理解流程处理一切文档](./assets/readme/formats/formats.zh-CN.webp)](#支持的输入)

不同格式进入同一套页面理解流程。正文、表格、公式、图表、图示关系、图像
含义和可见界面状态，最终组成一份可检索的 Markdown 文档。

## doc7 选择了另一条路线

| 主要路线 | 代表项目 | 处理方式 | 需要维护的东西 |
Expand All @@ -241,13 +270,6 @@ doc7 的 MIT 许可证范围。[查看来源和授权记录](./examples/attentio

模型由用户决定。doc7 不预设模型规模，也不会宣传没有实测过的模型效果。接入私有化开源模型后，不再需要购买专门的文档解析服务，也没有 doc7 处理额度。

## 命令行就是产品入口

[![doc7 在 macOS、Linux 和 Windows 上的命令行界面](./assets/readme/cli/cli.zh-CN.webp)](#快速开始)

同一个二进制文件提供交互式 CLI、批量处理、模型检查、MCP、Go SDK 和异步
HTTP 服务。

## 模型、依赖与失败恢复

先查看兼容 OpenAI 的视觉模型接口实际提供的模型 ID：
Expand Down
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