Stitch OCR Detections¶
Class: StitchOCRDetectionsBlockV1
Combines OCR detection results into a coherent text string by organizing detections spatially. This transformation is perfect for turning individual OCR results into structured, readable text!
How It Works¶
This transformation reconstructs the original text from OCR detection results by:
-
📐 Grouping text detections into rows based on their vertical (
y) positions -
📏 Sorting detections within each row by horizontal (
x) position -
📜 Concatenating the text in reading order (left-to-right, top-to-bottom)
Parameters¶
-
tolerance: Controls how close detections need to be vertically to be considered part of the same line of text. A higher tolerance will group detections that are further apart vertically. -
reading_direction: Determines the order in which text is read. Available options:-
"left_to_right": Standard left-to-right reading (e.g., English) ➡️
-
"right_to_left": Right-to-left reading (e.g., Arabic) ⬅️
-
"vertical_top_to_bottom": Vertical reading from top to bottom ⬇️
-
"vertical_bottom_to_top": Vertical reading from bottom to top ⬆️
-
"auto": Automatically detects the reading direction based on the spatial arrangement of text elements.
-
Why Use This Transformation?¶
This is especially useful for:
-
📖 Converting individual character/word detections into a readable text block
-
📝 Reconstructing multi-line text from OCR results
-
🔀 Maintaining proper reading order for detected text elements
-
🌏 Supporting different writing systems and text orientations
Example Usage¶
Use this transformation after an OCR model that outputs individual words or characters, so you can reconstruct the original text layout in its intended format.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/stitch_ocr_detections@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
reading_direction |
str |
The direction of the text in the image.. | ❌ |
tolerance |
int |
The tolerance for grouping detections into the same line of text.. | ✅ |
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 Stitch OCR Detections in version v1.
- inputs:
Detections Filter,Perspective Correction,VLM as Detector,Object Detection Model,Google Vision OCR,Path Deviation,Pixel Color Count,Detections Consensus,Object Detection Model,Byte Tracker,Byte Tracker,SIFT Comparison,Detection Offset,Line Counter,Detections Stabilizer,VLM as Detector,Distance Measurement,Image Contours,Template Matching,Detections Transformation,Detections Stitch,Byte Tracker,Time in Zone,Path Deviation,YOLO-World Model,Line Counter,SIFT Comparison,Time in Zone,Velocity,Detections Classes Replacement - outputs:
Segment Anything 2 Model,Cache Get,Stability AI Inpainting,Clip Comparison,Perspective Correction,Cache Set,Roboflow Custom Metadata,SIFT Comparison,CogVLM,Ellipse Visualization,OpenAI,Polygon Visualization,Trace Visualization,CLIP Embedding Model,Dot Visualization,Google Vision OCR,Polygon Zone Visualization,Roboflow Dataset Upload,Llama 3.2 Vision,Classification Label Visualization,Corner Visualization,Line Counter,Dynamic Crop,Reference Path Visualization,Label Visualization,Mask Visualization,Triangle Visualization,Line Counter Visualization,Model Monitoring Inference Aggregator,Time in Zone,Line Counter,Anthropic Claude,Instance Segmentation Model,Webhook Sink,Time in Zone,Instance Segmentation Model,Slack Notification,Path Deviation,Twilio SMS Notification,Roboflow Dataset Upload,Google Gemini,Model Comparison Visualization,Halo Visualization,Crop Visualization,Image Blur,Distance Measurement,Circle Visualization,Image Preprocessing,Background Color Visualization,Pixel Color Count,Size Measurement,Florence-2 Model,Bounding Box Visualization,Florence-2 Model,Local File Sink,LMM For Classification,Stability AI Image Generation,Image Threshold,Detections Stitch,LMM,Keypoint Visualization,Email Notification,Color Visualization,Path Deviation,YOLO-World Model,OpenAI
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Stitch OCR Detections in version v1 has.
Bindings
-
input
predictions(object_detection_prediction): The output of an OCR detection model..tolerance(integer): The tolerance for grouping detections into the same line of text..
-
output
ocr_text(string): String value.
Example JSON definition of step Stitch OCR Detections in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/stitch_ocr_detections@v1",
"predictions": "$steps.my_ocr_detection_model.predictions",
"reading_direction": "right_to_left",
"tolerance": 10
}