> Source: https://txtfetch.com/extract/docx
> Plain-text twin — every page on txtfetch.com has one. https://txtfetch.com/text

---

# Every Office format, one endpoint.

Word, PowerPoint, Excel, RTF, OpenDocument. Per-format libraries and screenshot-reading VLMs both struggle to cover that breadth in one pipeline.

the-problem

Office documents aren't one format. They're a family: .docx and the legacy .doc binary format, .pptx and .ppt, .xlsx and .xls, plus the OpenDocument siblings (.odt/.ods/.odp) and .rtf. A 'parse Office docs' feature usually means five or six separate libraries, each with its own quirks, versioning, and failure modes. Vision-based extractors treat every page as an image. That loses slide speaker notes, spreadsheet formulas, and the structural distinction between a table and a wall of text. Breadth across office formats is exactly where they're weakest.

one-request-solution

txtfetch wraps Apache Tika's Office parsers behind one request. POST any file in the family, or point it at a URL. You get the same { status, extracted\_text } response, with slide notes, sheet contents, and body text included.

curl

```curl
curl -X POST https://api.txtfetch.com/v1/extract \
  -H "Authorization: Bearer $TXTFETCH_KEY" \
  -F file=@board-deck.pptx
```

Python

```python
import os
import requests

with open("board-deck.pptx", "rb") as f:
    r = requests.post(
        "https://api.txtfetch.com/v1/extract",
        headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
        files={"file": f},
    )

print(r.json()["extracted_text"])
```

JavaScript

```javascript
import { readFile } from "node:fs/promises";

const file = new Blob([await readFile("board-deck.pptx")]);
const form = new FormData();
form.append("file", file, "board-deck.pptx");

const res = await fetch("https://api.txtfetch.com/v1/extract", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
  body: form,
});

const { extracted_text } = await res.json();
console.log(extracted_text);
```

Go

```go
package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"mime/multipart"
	"net/http"
	"os"
)

type extractResponse struct {
	Status        string `json:"status"`
	ExtractedText string `json:"extracted_text"`
}

func main() {
	f, err := os.Open("board-deck.pptx")
	if err != nil {
		panic(err)
	}
	defer f.Close()

	var body bytes.Buffer
	writer := multipart.NewWriter(&body)
	part, err := writer.CreateFormFile("file", "board-deck.pptx")
	if err != nil {
		panic(err)
	}
	if _, err := io.Copy(part, f); err != nil {
		panic(err)
	}
	writer.Close()

	req, err := http.NewRequest("POST", "https://api.txtfetch.com/v1/extract", &body)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Authorization", "Bearer "+os.Getenv("TXTFETCH_KEY"))
	req.Header.Set("Content-Type", writer.FormDataContentType())

	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	var result extractResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		panic(err)
	}
	fmt.Println(result.ExtractedText)
}
```

Or skip the download. Pass a `url` parameter and txtfetch fetches the document server-side:

curl

```curl
curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/sales-workbook.xlsx" \
  -H "Authorization: Bearer $TXTFETCH_KEY"
```

Python

```python
import os
import requests

r = requests.post(
    "https://api.txtfetch.com/v1/extract",
    headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
    params={"url": "https://example.com/sales-workbook.xlsx"},
)

print(r.json()["extracted_text"])
```

JavaScript

```javascript
const endpoint = new URL("https://api.txtfetch.com/v1/extract");
endpoint.searchParams.set("url", "https://example.com/sales-workbook.xlsx");

const res = await fetch(endpoint, {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
});

const { extracted_text } = await res.json();
console.log(extracted_text);
```

Go

```go
package main

import (
	"encoding/json"
	"fmt"
	"net/http"
	"net/url"
	"os"
)

type extractResponse struct {
	Status        string `json:"status"`
	ExtractedText string `json:"extracted_text"`
}

func main() {
	endpoint, err := url.Parse("https://api.txtfetch.com/v1/extract")
	if err != nil {
		panic(err)
	}
	q := endpoint.Query()
	q.Set("url", "https://example.com/sales-workbook.xlsx")
	endpoint.RawQuery = q.Encode()

	req, err := http.NewRequest("POST", endpoint.String(), nil)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Authorization", "Bearer "+os.Getenv("TXTFETCH_KEY"))

	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	var result extractResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		panic(err)
	}
	fmt.Println(result.ExtractedText)
}
```

```
{
  "status": "success",
  "extracted_text": "..."
}
```

what-comes-back

That is the shape. This is the text. A real .docx from the benchmark corpus, word-diffed against the raw parser output. Nothing here was written for this page. Every character comes from a committed fixture.

Q3 2026 Board Update (DOCX)

`board-update-q3-2026.docx` · application/vnd.openxmlformats-officedocument.wordprocessingml.document · left pane read via Apache Tika, **right pane: the human-checked expected.json**

raw parser output

```
Q3 2026 Board Update

This update covers Q3 2026 performance across all product lines.

Highlights

Annual recurring revenue grew 14% quarter over quarter
Shipped the structured-output beta
Markdown mode
Element JSON mode
Support headcount increased to 6 engineers

Revenue by Plan

Plan	MRR	Growth
Hobby	$0	n/a
Developer	$18,200	11%
Scale	$41,900	19%

Full detail is available in the appendix & supporting spreadsheets.
```

the human-checked ideal

```
Q3 2026 Board Update

This update covers Q3 2026 performance across all product lines.

Highlights

Annual recurring revenue grew 14% quarter over quarter
Shipped the structured-output beta

Markdown mode
Element JSON mode
Support headcount increased to 6 engineers

Revenue by Plan

Plan	MRR	Growth
Hobby	$0	n/a
Developer	$18,200	11%
Scale	$41,900	19%

Full detail is available in the appendix & supporting spreadsheets.
```

[See all ten documents, with the full explanation →](https://txtfetch.com/diff)

formats-covered

-   `.docx`
-   `.doc`
-   `.pptx`
-   `.ppt`
-   `.xlsx`
-   `.xls`
-   `.odt`
-   `.ods`
-   `.odp`
-   `.rtf`

faq

**How do I extract text from a .docx file?**: POST it as multipart form data to https://api.txtfetch.com/v1/extract with your API key in the Authorization header. The response is { "status": "success", "extracted_text": "..." }.

**Can it read PowerPoint speaker notes, not just slide text?**: Yes. Apache Tika extracts slide body text and speaker notes from .pptx/.ppt files. Both are included in the returned text.

**Does it handle the legacy .doc and .xls binary formats too?**: Yes. txtfetch covers both the modern XML-based Office formats and their legacy binary predecessors through the same endpoint.

**What about OpenDocument files (.odt, .ods, .odp)?**: Supported the same way. POST the file or its URL, and get back the same JSON response shape as any other format.

go-further

-   [Read the .docx guide →](https://txtfetch.com/blog/parse-office-docs-docx-pptx-xlsx-for-llms)
-   [Drop a real .docx and see the exact text it extracts to, free →](https://txtfetch.com/tools/docx-to-text)
-   [Need the .doc / .xls / .ppt 97-2003 story specifically? →](https://txtfetch.com/extract/legacy-office)
-   [API quickstart →](https://txtfetch.com/docs)
-   [How the pipeline works →](https://txtfetch.com/how-it-works)
-   [Extract it from your language →](https://txtfetch.com/for)
-   [Get an API key →](https://app.txtfetch.com/signup)

Office

-   [`.pptx`](https://txtfetch.com/extract/pptx)
-   [`.xlsx`](https://txtfetch.com/extract/xlsx)
-   [`.doc`](https://txtfetch.com/extract/legacy-office)
-   [`.rtf`](https://txtfetch.com/extract/rtf)
-   [All formats →](https://txtfetch.com/extract)

## Send a real .docx through it.

One HTTP call returns the text. Read one in your browser first, for free.

[Get an API key →](https://app.txtfetch.com/signup)

[Open the free .docx reader →](https://txtfetch.com/tools/docx-to-text)