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:
Perspective Correction,Stability AI Inpainting,Image Convert Grayscale,Morphological Transformation,Pixelate Visualization,Stitch Images,QR Code Generator,Image Slicer,Image Preprocessing,SIFT,Line Counter Visualization,Polygon Zone Visualization,Image Threshold,Image Slicer,Corner Visualization,Dynamic Crop,Stability AI Outpainting,Halo Visualization,Heatmap Visualization,Keypoint Visualization,Color Visualization,Blur Visualization,Stability AI Image Generation,Camera Focus,Label Visualization,Classification Label Visualization,Camera Focus,Camera Calibration,Morphological Transformation,Trace Visualization,Contrast Enhancement,Bounding Box Visualization,Reference Path Visualization,Depth Estimation,Halo Visualization,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,SIFT Comparison,Image Contours,Mask Visualization,Relative Static Crop,Crop Visualization,Background Subtraction,Circle Visualization,Text Display,Polygon Visualization,Background Color Visualization,Absolute Static Crop,Image Blur,Polygon Visualization,Contrast Equalization,Grid Visualization,Icon Visualization,Triangle Visualization - outputs:
S3 Sink,Email Notification,Keypoint Detection Model,Morphological Transformation,Path Deviation,Qwen-VL,Clip Comparison,SAM 3,Twilio SMS/MMS Notification,YOLO-World Model,Line Counter,Time in Zone,MoonshotAI Kimi,Stitch OCR Detections,Polygon Zone Visualization,OpenAI-Compatible LLM,OpenAI,Heatmap Visualization,Email Notification,Keypoint Visualization,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Seg Preview,Google Vision OCR,Label Visualization,SAM 3,Instance Segmentation Model,Path Deviation,Overlap Filter,Local File Sink,Google Gemini,Byte Tracker,Background Color Visualization,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Moondream2,Velocity,SIFT Comparison,Detection Event Log,Florence-2 Model,Time in Zone,Single-Label Classification Model,Detections Filter,Detections Merge,Detections Stabilizer,LMM For Classification,Image Preprocessing,Roboflow Dataset Upload,Segment Anything 2 Model,Stability AI Outpainting,Corner Visualization,Halo Visualization,Time in Zone,Semantic Segmentation Model,Blur Visualization,Detections List Roll-Up,Perception Encoder Embedding Model,Distance Measurement,Morphological Transformation,Trace Visualization,Stitch OCR Detections,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,Pixel Color Count,Text Display,Detections Combine,ByteTrack Tracker,Florence-2 Model,Byte Tracker,Icon Visualization,Mask Area Measurement,Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Stability AI Inpainting,Line Counter,QR Code Generator,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Image Threshold,OC-SORT Tracker,Anthropic Claude,Dynamic Crop,Size Measurement,Detections Consensus,Cache Set,Bounding Box Visualization,Depth Estimation,Detection Offset,CLIP Embedding Model,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Image Blur,Anthropic Claude,Per-Class Confidence Filter,Triangle Visualization,Roboflow Custom Metadata,OpenAI,Slack Notification,Pixelate Visualization,OpenAI,Instance Segmentation Model,Line Counter Visualization,Detections Classes Replacement,Cache Get,LMM,Roboflow Dataset Upload,Detections Transformation,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Detections Stitch,Byte Tracker,Ellipse Visualization,PTZ Tracking (ONVIF),SORT Tracker,Mask Visualization,GLM-OCR,Crop Visualization,CogVLM,Circle Visualization,SAM2 Video Tracker,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma
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>"
}