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:
Distance Measurement,Time in Zone,Detections Filter,Time in Zone,Object Detection Model,Dynamic Crop,VLM as Detector,Template Matching,Byte Tracker,Detections Classes Replacement,Image Contours,SIFT Comparison,Detections Transformation,Object Detection Model,Velocity,Line Counter,SIFT Comparison,Detections Stitch,Google Vision OCR,Path Deviation,Detections Merge,YOLO-World Model,Detection Offset,Byte Tracker,VLM as Detector,Detections Stabilizer,Byte Tracker,Line Counter,Perspective Correction,Detections Consensus,Path Deviation,Pixel Color Count - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,LMM,Polygon Zone Visualization,Image Blur,Cache Set,Dot Visualization,Path Deviation,Google Gemini,Roboflow Dataset Upload,Stability AI Inpainting,Line Counter,OpenAI,Distance Measurement,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,CogVLM,Instance Segmentation Model,Email Notification,Classification Label Visualization,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Ellipse Visualization,Size Measurement,Pixel Color Count,Cache Get,Bounding Box Visualization,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Local File Sink,Anthropic Claude,Crop Visualization,Detections Stitch,YOLO-World Model,Model Comparison Visualization,Perspective Correction,OpenAI,Path Deviation,Mask Visualization,Time in Zone,Clip Comparison,Time in Zone,Dynamic Crop,Florence-2 Model,Instance Segmentation Model,Florence-2 Model,Reference Path Visualization,CLIP Embedding Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,Keypoint Visualization,LMM For Classification,Roboflow Custom Metadata,Line Counter,Segment Anything 2 Model
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
}