EasyOCR¶
Class: EasyOCRBlockV1
Source: inference.core.workflows.core_steps.models.foundation.easy_ocr.v1.EasyOCRBlockV1
Retrieve the characters in an image using EasyOCR 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.
Note that EasyOCR has limitations running within containers on Apple Silicon.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/easy_ocr@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Unique name of step in workflows. | ❌ |
language |
str |
Language model to use for OCR. | ❌ |
quantize |
bool |
Quantized models are smaller and faster, but may be less accurate and won't work correctly on all hardware.. | ❌ |
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 EasyOCR in version v1.
- inputs:
Halo Visualization,Dynamic Crop,Color Visualization,Stability AI Outpainting,Morphological Transformation,Image Preprocessing,Relative Static Crop,Image Blur,Line Counter Visualization,Halo Visualization,Blur Visualization,Morphological Transformation,Stability AI Inpainting,Perspective Correction,Keypoint Visualization,Text Display,Image Threshold,Icon Visualization,Triangle Visualization,Pixelate Visualization,Image Slicer,Camera Calibration,Model Comparison Visualization,Depth Estimation,Trace Visualization,Ellipse Visualization,Crop Visualization,Dot Visualization,Circle Visualization,Reference Path Visualization,Polygon Zone Visualization,Contrast Equalization,Camera Focus,Heatmap Visualization,Background Subtraction,Image Contours,Polygon Visualization,Background Color Visualization,Absolute Static Crop,Classification Label Visualization,Bounding Box Visualization,Label Visualization,QR Code Generator,Camera Focus,SIFT Comparison,Contrast Enhancement,Grid Visualization,Stitch Images,Corner Visualization,Mask Visualization,Image Slicer,SIFT,Stability AI Image Generation,Polygon Visualization,Image Convert Grayscale - outputs:
Detections Classes Replacement,Morphological Transformation,Image Preprocessing,Email Notification,Morphological Transformation,Halo Visualization,Detections Transformation,Pixel Color Count,Time in Zone,Text Display,BoT-SORT Tracker,Image Threshold,Model Monitoring Inference Aggregator,Pixelate Visualization,Time in Zone,Qwen-VL,OpenAI,CogVLM,Crop Visualization,SAM 3,Dot Visualization,Google Vision OCR,Detections Merge,Detections List Roll-Up,Florence-2 Model,Roboflow Dataset Upload,Qwen3.5-VL,Roboflow Vision Events,Polygon Zone Visualization,Polygon Visualization,S3 Sink,Twilio SMS/MMS Notification,QR Code Generator,SIFT Comparison,Per-Class Confidence Filter,Cache Get,Stitch OCR Detections,Detections Filter,Byte Tracker,Color Visualization,Roboflow Dataset Upload,Roboflow Custom Metadata,LMM For Classification,OpenAI-Compatible LLM,Stability AI Inpainting,Image Blur,Line Counter Visualization,Object Detection Model,Detections Stabilizer,Blur Visualization,Path Deviation,Current Time,SAM 3,Perspective Correction,Keypoint Visualization,Byte Tracker,Detection Offset,Anthropic Claude,MQTT Writer,MoonshotAI Kimi,Google Gemini,SAM 3,Depth Estimation,Detections Stitch,Ellipse Visualization,Google Gemma API,Detections Consensus,Slack Notification,Overlap Analysis,Time in Zone,Google Gemini,Cache Set,Label Visualization,Bounding Box Visualization,Keypoint Detection Model,Stitch OCR Detections,Size Measurement,Multi-Label Classification Model,OpenAI,Perception Encoder Embedding Model,Anthropic Claude,Roboflow Asset Library Attributes,Moondream2,OC-SORT Tracker,CLIP Embedding Model,Florence-2 Model,Seg Preview,Overlap Filter,YOLO-World Model,Segment Anything 2 Model,Twilio SMS Notification,Local File Sink,Single-Label Classification Model,Triangle Visualization,Icon Visualization,Qwen 3.5 API,Mask Area Measurement,Path Deviation,OpenRouter,Instance Segmentation Model,Distance Measurement,Instance Segmentation Model,OpenAI,Background Color Visualization,MoonshotAI Kimi,Byte Tracker,Google Gemini,Corner Visualization,Reference Path Visualization,Line Counter,Halo Visualization,Webhook Sink,Dynamic Crop,Instance Segmentation Model,Stability AI Outpainting,Detection Event Log,Anthropic Claude,Clip Comparison,SORT Tracker,OpenAI,Llama 3.2 Vision,ByteTrack Tracker,Velocity,Detections Combine,Trace Visualization,Model Comparison Visualization,Google Gemma,PTZ Tracking (ONVIF),OPC UA Writer Sink,Line Counter,Circle Visualization,Email Notification,LMM,Event Writer,Instance Segmentation Model,Contrast Equalization,Heatmap Visualization,Camera Focus,GLM-OCR,Qwen 3.6 API,SAM2 Video Tracker,Llama 3.2 Vision,Classification Label Visualization,Mask Visualization,Microsoft SQL Server Sink,Stability AI Image Generation,Semantic Segmentation Model,Polygon Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
EasyOCR 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 EasyOCR in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/easy_ocr@v1",
"images": "$inputs.image",
"language": "<block_does_not_provide_example>",
"quantize": "<block_does_not_provide_example>"
}