# Extract text from a list of image links

**Use case:** 

The text of four public sample images given by link: a printed page of poetry, a page of test text, a short caption and an escaped-characters sample. One row per image with the clean text, the confidence, the word count and the real size of the file. Replace the links with your own images.

## Input

```json
{
  "startUrls": [
    "https://tesseract.projectnaptha.com/img/eng_bw.png",
    "https://raw.githubusercontent.com/naptha/tesseract.js/master/tests/assets/images/testocr.png",
    "https://raw.githubusercontent.com/naptha/tesseract.js/master/tests/assets/images/cosmic.png",
    "https://raw.githubusercontent.com/naptha/tesseract.js/master/tests/assets/images/escape_chars.png"
  ],
  "languages": [
    "eng"
  ],
  "maxItems": 500,
  "minWidth": 80,
  "minHeight": 80,
  "maxImagesPerPage": 20,
  "maxImageMegabytes": 15,
  "pageSegmentationMode": "auto",
  "minConfidence": 40,
  "includeLines": false,
  "onlyNew": false,
  "stateName": "default",
  "notifyWhenEmpty": false,
  "requestIntervalMs": 1000
}
```

## Output

```json
{
  "imageUrl": {
    "label": "Address of the image file that was read",
    "format": "string"
  },
  "text": {
    "label": "The text of the image, one line of the picture per line; spaces cleaned, e-mails and phone numbers masked, at most 100,000 characters (null when no text was found)",
    "format": "string"
  },
  "meanConfidence": {
    "label": "Mean confidence of the OCR engine, 0 to 100 (null when the engine found nothing at all)",
    "format": "number"
  },
  "wordCount": {
    "label": "Number of words in the text",
    "format": "integer"
  },
  "textFound": {
    "label": "True when text was found (the row is charged); false when the image holds no readable text (the row is free)",
    "format": "boolean"
  },
  "languages": {
    "label": "Languages the engine was asked to read, as text",
    "format": "string"
  },
  "width": {
    "label": "Width of the image in pixels, read from the file",
    "format": "integer"
  },
  "height": {
    "label": "Height of the image in pixels, read from the file",
    "format": "integer"
  },
  "format": {
    "label": "Image format read from the file: png, jpeg, gif, webp, bmp or tiff",
    "format": "string"
  },
  "pageUrl": {
    "label": "Page the image was found on (null for direct image links)",
    "format": "string"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The Image to Text OCR API: Extract Text from Images in Bulk Actor is ready to run with the inputs already set for "Extract text from a list of image links". [Sign up or log in](https://console.apify.com/create-task-from-example/DOWzTMzu8ktRiDws5) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Extract text from a list of image links" task

The Image to Text OCR API: Extract Text from Images in Bulk Actor will start running based on the input automatically. You can also change all the input fields, like image or page urls, languages in the images, and more, to match your specific use case.

### 3. Receive the "Extract text from a list of image links" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as address of the image file that was read, the text of the image, one line of the picture per line; spaces cleaned, e-mails and phone numbers masked, at most 100,000 characters (null when no text was found), mean confidence of the ocr engine, 0 to 100 (null when the engine found nothing at all), and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Extract text from a list of image links" task via API](https://apify.com/rowcraft/image-ocr-text-extractor.md).

## About this Actor

This example demonstrates how to use [Image to Text OCR API: Extract Text from Images in Bulk](https://apify.com/rowcraft/image-ocr-text-extractor.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/rowcraft/image-ocr-text-extractor.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/rowcraft/image-ocr-text-extractor.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).