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