Text Detection

This function block finds text regions in an image and visualizes them. It is tuned for detecting oriented text areas (rotated or tilted) and returns both a preview image and structured location data for further processing.

📥 Inputs

Image Any Input image to be analyzed for text.

📤 Outputs

Image Any Annotated image with detected text regions drawn.

Referance Point List of reference points (corner points) for each detected text region.

Referance Rectangles List of bounding rectangles for each detected text region.

Number of Detected Text Total number of text regions found.

🕹️ Controls

Confidence Adjusts the minimum confidence required for a detection to be accepted. Higher values reduce false positives but may miss faint text.

NMS Threshold Adjusts how overlapping detections are merged. Lower values make merging stricter, reducing duplicate boxes over the same text.

⚙️ Running mechanism

When executed, the block examines the provided image, searches for regions that look like text, filters results by confidence, merges overlapping detections, and scales found regions back to the input image size. The block then outputs an annotated preview image, a list of reference points and rectangles for each detection, and the total count of detected text regions.

🎯 Features

• Detects rotated and angled text regions, not just horizontal lines.

• Provides both visual feedback (annotated image) and structured outputs (points, rectangles, count) for downstream processing.

• Adjustable sensitivity via Confidence and NMS Threshold controls to tune precision vs recall.

📝 Usage instructions

1. Provide an image to the Image Any input (from a camera, file loader or stream).

2. Adjust the Confidence slider to balance false positives vs missed text.

3. Adjust the NMS Threshold slider if multiple overlapping boxes appear over the same text area.

4. Use the annotated Image Any output to preview detections, and use Referance Point / Referance Rectangles for extraction, cropping, or passing to recognition blocks.

💡 Tips and Tricks

• If the image is very large, use Image Resizer to downscale for faster processing, then map rectangle coordinates back to the original if needed.

• For actual text recognition after detection, connect the detected crop areas to OCR or OCR (EasyOCR) blocks.

• To focus on a specific area, crop first with Image ROI or Image ROI Select and feed the cropped image into this block.

• Use Show Image to preview the annotated output, and Draw Result On Image to overlay custom status text based on detection results.

• Save results with Image Write or log examples with Image Logger for later review.

🛠️ Troubleshooting

• No detections: Lower the Confidence value or provide a clearer.

• Too many small boxes or duplicates: Increase the Confidence value and lower the NMS Threshold to merge overlapping detections.

• Detections misplaced after resizing: Ensure any resizing steps are compensated when interpreting Referance Rectangles coordinates.

• If you only need recognized text (not locations), chain this block to OCR / OCR (EasyOCR) and use the count output to gate downstream logic.