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@v1
to 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:
Line Counter
,Detection Offset
,SIFT Comparison
,Line Counter
,Detections Transformation
,Byte Tracker
,SIFT Comparison
,Detections Stabilizer
,Detections Classes Replacement
,Detections Filter
,VLM as Detector
,Object Detection Model
,Template Matching
,Time in Zone
,Detections Consensus
,Path Deviation
,YOLO-World Model
,Overlap Filter
,Pixel Color Count
,Image Contours
,Dynamic Crop
,Perspective Correction
,Moondream2
,Detections Merge
,Distance Measurement
,Google Vision OCR
,Velocity
,Time in Zone
,Detections Stitch
,Byte Tracker
,Object Detection Model
,VLM as Detector
,Path Deviation
,Byte Tracker
- outputs:
Line Counter
,Polygon Zone Visualization
,Slack Notification
,Time in Zone
,Local File Sink
,YOLO-World Model
,Instance Segmentation Model
,Trace Visualization
,Roboflow Custom Metadata
,Perspective Correction
,OpenAI
,Distance Measurement
,Circle Visualization
,Clip Comparison
,OpenAI
,Triangle Visualization
,Halo Visualization
,Line Counter
,Size Measurement
,Corner Visualization
,Email Notification
,LMM
,Roboflow Dataset Upload
,Dynamic Crop
,Cache Set
,Model Monitoring Inference Aggregator
,Cache Get
,Segment Anything 2 Model
,Llama 3.2 Vision
,Anthropic Claude
,Model Comparison Visualization
,Crop Visualization
,CLIP Embedding Model
,CogVLM
,Image Threshold
,Stability AI Inpainting
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Path Deviation
,Pixel Color Count
,Color Visualization
,Twilio SMS Notification
,Classification Label Visualization
,Google Vision OCR
,Label Visualization
,Time in Zone
,Reference Path Visualization
,Webhook Sink
,Google Gemini
,Roboflow Dataset Upload
,Line Counter Visualization
,Image Blur
,Florence-2 Model
,SIFT Comparison
,LMM For Classification
,Instance Segmentation Model
,Polygon Visualization
,Florence-2 Model
,Mask Visualization
,Ellipse Visualization
,Detections Stitch
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,Path Deviation
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
}