--- title: "Extracting data from images using ICR | Nutrient Python SDK" canonical_url: "https://www.nutrient.io/guides/python/extraction/extract-data-from-image-icr/" md_url: "https://www.nutrient.io/guides/python/extraction/extract-data-from-image-icr.md" last_updated: "2026-06-09T10:32:42.848Z" description: "Extract structured data from images using local ICR with Nutrient Python SDK. Offline processing for air-gapped environments without API calls." --- # Extracting data from images using ICR Use ICR to extract structured document data from images with local models. Common use cases include: - Air-gapped document processing - Privacy-sensitive workflows with local-only processing - High-volume extraction with predictable runtime cost - Pipelines that need layout and semantic structure ICR returns more than plain text. It detects layout and semantic elements such as tables, key-value regions, headings, and equations. [Download sample](https://www.nutrient.io/downloads/samples/python/extract-data-from-image-icr.zip) ## How Nutrient helps Nutrient Python SDK handles local model loading, layout analysis, and JSON output generation. The SDK handles: - Local model deployment and loading details - Table detection and cell boundary extraction - Semantic element classification and hierarchy parsing - Bounding box and reading-order calculation ## Prerequisites Before following this guide, ensure you have: - Python 3.8 or higher installed - Nutrient Python SDK installed (`pip install nutrient-sdk`) - An image file to process (PNG, JPEG, or other supported formats) - Basic familiarity with Python [context manager](https://docs.python.org/3/reference/datamodel.html#context-managers) and the `with` statement For initial SDK setup and configuration, refer to the [getting started](https://www.nutrient.io/sdk/python/getting-started.md) guide. ## Complete implementation This example extracts structured JSON from an image using the ICR engine: ```python from nutrient_sdk import Document, Vision, VisionEngine ``` ### Configuring ICR mode Open the image and set the vision engine to ICR. In this sample: - The document opens in a [context manager](https://docs.python.org/3/reference/datamodel.html#context-managers). - `document.settings.vision_settings.engine = VisionEngine.ICR` sets local ICR mode. - ICR is the default engine, so this step is optional. > ICR is the default engine, so this property assignment is optional but shown here for illustration purposes. ```python with Document.open("input_ocr_multiple_languages.png") as document: # Configure ICR engine for local processing (this is the default) document.settings.vision_settings.engine = VisionEngine.ICR ``` ### Creating a vision instance and extracting content Create a vision instance and call `extract_content()`. In this sample: - `Vision.set(document)` binds extraction to the opened document. - `extract_content()` returns structured JSON as a string. - Processing runs locally when the engine is ICR. ```python vision = Vision.set(document) content_json = vision.extract_content() ``` Write the JSON string to a file for downstream use. Use the output for storage, indexing, or custom analysis: ```python with open("output.json", "w") as f: f.write(content_json) ``` ### Performance tip: Preload Vision resources The first ICR run may take longer because required Vision resources/models are downloaded and initialized on first use. To avoid this startup delay during extraction, call `vision.warmup()` once before `extract_content()`. ```python with Document.open("input_ocr_multiple_languages.png") as document: document.settings.vision_settings.engine = VisionEngine.ICR vision = Vision.set(document) # Preload/download required Vision resources (first run can take time) vision.warmup() # Actual extraction content_json = vision.extract_content() ``` After warmup completes successfully, subsequent extractions are typically faster because resources are already cached locally. ## Understanding the output `extract_content()` returns structured JSON with layout and semantic information. ICR output includes: - **Document elements** — Paragraphs, headings, tables, figures, and equations - **Bounding boxes** — Pixel coordinates for detected regions - **Reading order** — Element order for content flow reconstruction - **Element classification** — Semantic labels such as paragraph, table, and heading - **Hierarchical structure** — Parent-child relationships across sections and blocks Use this JSON for extraction pipelines, structured storage, and search indexing. ## Error handling Vision API raises `VisionException` when extraction fails. Common failure scenarios include: - The image file can’t be read because of path or permission issues. - Image data is corrupted or truncated. - ICR models are missing or inaccessible. - Available memory is insufficient for model loading. - Image format or encoding is unsupported. In production code: - Catch `VisionException`. - Return a clear error message. - Log failure details for debugging. ## Conclusion Use this workflow for ICR-based extraction: 1. Open the image document using a [context manager](https://docs.python.org/3/reference/datamodel.html#context-managers) for automatic resource cleanup. 2. Configure the vision settings with the `engine` property assigned to `VisionEngine.ICR` for local AI processing. 3. ICR is the default engine, making this configuration optional but useful for explicit control. 4. Create a vision instance with `Vision.set()` to bind content extraction operations to the document. 5. Call `extract_content()` to invoke local AI models for document layout analysis. 6. The ICR engine loads AI models, detects semantic elements (tables, equations, headings), and determines reading order. 7. The method returns a JSON-formatted string containing complete document structure with bounding boxes in pixel coordinates. 8. All processing occurs locally without external API calls, ensuring data privacy and offline capability. 9. Write the JSON content to a file using Python’s built-in file handling with [context manager](https://docs.python.org/3/reference/datamodel.html#context-managers) syntax. 10. Handle `VisionException` errors for robust error recovery in production environments. 11. The JSON output enables integration with downstream pipelines, including data extraction, database storage, and search indexing. 12. ICR mode is ideal for air-gapped environments, sensitive document processing, and high-volume workflows. For related image extraction workflows, refer to the [Python SDK guides](https://www.nutrient.io/guides/python.md). Download [this ready-to-use sample package](https://www.nutrient.io/downloads/samples/python/extract-data-from-image-icr.zip) to explore the Vision API capabilities with preconfigured ICR settings. --- ## Related pages - [Speeding up first ICR operation by predownloading models](/guides/python/extraction/speed-up-first-icr-by-downloading-requirements.md) - [Extracting text from PDF documents](/guides/python/extraction/pdf-to-text.md) - [Extracting text from multilingual images](/guides/python/extraction/read-text-from-image-multi-language.md) - [Extracting structured data from documents](/guides/python/extraction/extract-structured-data.md) - [Generating image descriptions using Claude](/guides/python/extraction/describe-image-with-claude.md) - [Extracting data from images using vision language models](/guides/python/extraction/extract-data-from-image-vlm.md) - [Generating image descriptions using OpenAI](/guides/python/extraction/describe-image-with-openai.md) - [Extracting text from images](/guides/python/extraction/read-text-from-image.md) - [Generating image descriptions using local AI](/guides/python/extraction/describe-image-with-local-ai.md) - [Nutrient Python SDK extraction guides](/guides/python/extraction.md) - [Applying OCR to a PDF document](/guides/python/extraction/apply-ocr-to-pdf.md) - [Extracting form fields from images](/guides/python/extraction/extract-form-fields-from-image.md) - [Extracting data from images using OCR](/guides/python/extraction/extract-data-from-image-ocr.md) - [Applying OCR to a PDF page](/guides/python/extraction/apply-ocr-to-pdf-page.md) - [Labeling form fields with a vision language model](/guides/python/extraction/label-form-fields-with-vlm.md) - [Extracting structured JSON data from PDF documents](/guides/python/extraction/json-data-extraction.md)