OCR

This function block extracts text from images and returns recognized text, bounding boxes, and an optional annotated image. It is designed for simple drag-and-drop pipelines: feed an image, set confidence threshold and options, then consume the text or visualize results.

📥 Inputs

Image This input accepts any image you want to perform text recognition on.

📤 Outputs

Result Annotated image with detection boxes and optional text overlay.

Whole Text All detected text joined as a single string.

Texts Individual detected text strings (multiple outputs possible).

Boxes Detected bounding boxes for each text region.

🕹️ Controls

Use auto rotation Toggle to enable automatic rotation handling for upside-down / rotated text.

Show Texts Toggle to draw recognized text labels on the annotated output image.

Threshold Slider to set minimum confidence for accepted detections (higher = fewer, more confident results).

🎨 Features

• Real-time text detection and recognition from images provided to the Image input.

• Confidence filtering using the Threshold slider to ignore weak detections.

• Optional image annotation: draw boxes and text on the returned Result image when Show Texts is enabled.

• Auto-rotation support to improve recognition on rotated or upside-down text when Use auto rotation is enabled.

• Outputs are provided in both human-readable string form (Whole Text / Texts) and structured geometry form (Boxes) for downstream processing.

⚙️ Running mechanism

• Provide an image to the Image input and run the scenario.

• The block processes the image and filters detections below the chosen Threshold.

• If any outputs that require visualization are connected, the block returns an annotated Result image with boxes and optional text overlays.

• Text outputs (Whole Text, Texts) and geometry outputs (Boxes) are produced for use by other blocks or export tools.

• Note: the first run may take longer (model or resources are initialized on demand). Afterwards, inference runs faster.

📝 Usage instructions

1. Feed the image you want to read into the Image input.

2. Set the Threshold slider to filter out low-confidence recognitions. Start with moderate values (e.g., 30–50) and adjust.

3. Enable Use auto rotation if your images may contain rotated text.

4. Enable Show Texts if you want the returned Result image to show detected text labels.

5. Use the Whole Text, Texts or Boxes outputs as needed (for display, logging, or logic).

💡 Tips and Tricks

• Improve accuracy by cropping the area that contains text first with Image ROI Select so the block only processes the region of interest.

• Speed up inference on large images by using Image Resize before feeding the image into this block.

• Remove complex backgrounds or isolate text regions with Background Removal (RMBG-1.4) or Background Removal (BiRefNet) when text sits on cluttered backgrounds.

• Reduce visual noise with Blur or convert to a cleaner binary image with Image Threshold when working with noisy captures.

• Quickly preview OCR results by sending the annotated Result image to Show Image.

• If you want an alternative OCR approach, try the OCR (EasyOCR) block and compare results for your dataset.

• Save recognized text or images downstream with Image Logger, Image Write, or CSV Export for traceability.

🛠️ Troubleshooting

• No text detected: increase the input image quality (crop to text, increase resolution) or lower the Threshold slightly.

• Many false detections: raise the Threshold and crop the image to focus on real text regions.

• Rotated text not read correctly: enable Use auto rotation or pre-rotate the image with Image AutoRotator or an Image Resize + rotate step.

• Slow initial run: the first evaluation may take longer while resources initialize. Subsequent runs will be faster.

• If you only need bounding geometry for logic (not annotated images), disconnect visualization outputs to reduce processing overhead.