OCR Model

Class: OCRModelBlockV1

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

Retrieve the characters in an image using DocTR 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: Polygon Visualization, Grid Visualization, Background Color Visualization, Stitch Images, Image Slicer, Corner Visualization, Trace Visualization, Classification Label Visualization, Camera Calibration, Mask Visualization, Bounding Box Visualization, Model Comparison Visualization, Image Convert Grayscale, Pixelate Visualization, Polygon Zone Visualization, Relative Static Crop, Crop Visualization, Ellipse Visualization, Triangle Visualization, Image Slicer, Camera Focus, Icon Visualization, QR Code Generator, Color Visualization, Label Visualization, Keypoint Visualization, Contrast Equalization, Image Contours, Blur Visualization, Dot Visualization, Circle Visualization, Stability AI Image Generation, Perspective Correction, Absolute Static Crop, Reference Path Visualization, Morphological Transformation, Image Blur, Image Threshold, SIFT, Line Counter Visualization, Depth Estimation, Stability AI Outpainting, Halo Visualization, Stability AI Inpainting, Image Preprocessing, Dynamic Crop, SIFT Comparison
• outputs: LMM, Background Color Visualization, Size Measurement, Model Monitoring Inference Aggregator, Corner Visualization, Detections Transformation, Detections Classes Replacement, Mask Visualization, CLIP Embedding Model, Line Counter, Local File Sink, Model Comparison Visualization, Email Notification, Pixelate Visualization, Time in Zone, Anthropic Claude, Google Gemini, Florence-2 Model, Ellipse Visualization, Triangle Visualization, Segment Anything 2 Model, QR Code Generator, Byte Tracker, Label Visualization, Time in Zone, Pixel Color Count, Roboflow Custom Metadata, Cache Set, Florence-2 Model, LMM For Classification, Blur Visualization, Dot Visualization, Overlap Filter, Stability AI Image Generation, Detection Offset, Perspective Correction, Google Vision OCR, Llama 3.2 Vision, Line Counter, Detections Stabilizer, Slack Notification, SAM 3, Morphological Transformation, Velocity, Image Blur, Clip Comparison, Image Threshold, Stitch OCR Detections, Stability AI Outpainting, Byte Tracker, Halo Visualization, Stability AI Inpainting, Polygon Visualization, OpenAI, Roboflow Dataset Upload, Path Deviation, Distance Measurement, CogVLM, Classification Label Visualization, Trace Visualization, Email Notification, Instance Segmentation Model, Bounding Box Visualization, Byte Tracker, Polygon Zone Visualization, Perception Encoder Embedding Model, OpenAI, Detections Stitch, Crop Visualization, Cache Get, YOLO-World Model, Detections Merge, Moondream2, Icon Visualization, Seg Preview, Color Visualization, Path Deviation, Roboflow Dataset Upload, Keypoint Visualization, Contrast Equalization, Instance Segmentation Model, Detections Filter, Circle Visualization, OpenAI, Time in Zone, Detections Combine, Reference Path Visualization, Twilio SMS Notification, Webhook Sink, Detections Consensus, Line Counter Visualization, PTZ Tracking (ONVIF).md), Image Preprocessing, Dynamic Crop, SIFT Comparison

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.
  • predictions (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.
  • 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"
}