OCR Model

Class: OCRModelBlockV1

Source: inference.core.workflows.core_steps.models.foundation.ocr.v1.OCRModelBlockV1

Retrieve the characters in an image using Optical Character Recognition (OCR).

This block returns the text within an image.

You may want to use this block in combination with a detections-based block (i.e. ObjectDetectionBlock). An object detection model could isolate specific regions from an image (i.e. a shipping container ID in a logistics use case) for further processing. You can then use a DynamicCropBlock to crop the region of interest before running OCR.

Using a detections model then cropping detections allows you to isolate your analysis on particular regions of an image.

Type identifier

Use the following identifier in step "type" field: roboflow_core/ocr_model@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Unique name of step in workflows.	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to OCR Model in version v1.

• inputs: Blur Visualization, Camera Focus, Image Threshold, Polygon Zone Visualization, Stability AI Inpainting, Relative Static Crop, Image Preprocessing, Keypoint Visualization, Background Color Visualization, Grid Visualization, Image Convert Grayscale, Trace Visualization, Absolute Static Crop, Color Visualization, Perspective Correction, Classification Label Visualization, Circle Visualization, Camera Calibration, Pixelate Visualization, Image Slicer, Label Visualization, Halo Visualization, Triangle Visualization, Reference Path Visualization, Image Slicer, Line Counter Visualization, Image Blur, Corner Visualization, SIFT Comparison, Image Contours, Dynamic Crop, Polygon Visualization, Depth Estimation, SIFT, Ellipse Visualization, Mask Visualization, Stitch Images, Bounding Box Visualization, Dot Visualization, Stability AI Image Generation, Model Comparison Visualization, Crop Visualization
• outputs: Line Counter, Polygon Zone Visualization, Slack Notification, Time in Zone, Local File Sink, YOLO-World Model, Instance Segmentation Model, Trace Visualization, Roboflow Custom Metadata, Perspective Correction, OpenAI, Distance Measurement, Circle Visualization, Clip Comparison, OpenAI, Triangle Visualization, Halo Visualization, Line Counter, Size Measurement, Corner Visualization, Email Notification, LMM, Roboflow Dataset Upload, Dynamic Crop, Cache Set, Model Monitoring Inference Aggregator, Cache Get, Segment Anything 2 Model, Llama 3.2 Vision, Anthropic Claude, Model Comparison Visualization, Crop Visualization, CLIP Embedding Model, CogVLM, Image Threshold, Stability AI Inpainting, Image Preprocessing, Keypoint Visualization, Background Color Visualization, Path Deviation, Pixel Color Count, Color Visualization, Twilio SMS Notification, Classification Label Visualization, Google Vision OCR, Label Visualization, Time in Zone, Reference Path Visualization, Webhook Sink, Google Gemini, Roboflow Dataset Upload, Line Counter Visualization, Image Blur, Florence-2 Model, SIFT Comparison, LMM For Classification, Instance Segmentation Model, Polygon Visualization, Florence-2 Model, Mask Visualization, Ellipse Visualization, Detections Stitch, Bounding Box Visualization, Dot Visualization, Stability AI Image Generation, Path Deviation

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds OCR Model in version v1 has.

Bindings

• input

  • images (image): The image to infer on..

• output

  • result (string): String value.
  • parent_id (parent_id): Identifier of parent for step output.
  • root_parent_id (parent_id): Identifier of parent for step output.
  • prediction_type (prediction_type): String value with type of prediction.

Example JSON definition of step OCR Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/ocr_model@v1",
    "images": "$inputs.image"
}