I totally get your frustration here—when you’ve spent time preprocessing images to look crisp and clean, it’s super disheartening when Tesseract still spits out inaccurate digit results. Let’s walk through some targeted adjustments you can try to boost your recognition accuracy:

1. Tweak the PSM (Page Segmentation Mode) #

You’re currently using --psm 13, which treats the image as a raw line of text. Depending on how your digits are laid out (e.g., single digits, tightly grouped numbers, or isolated characters), switching to a more specific PSM might help:

• Try --psm 8 (treats the image as a single word) if your target is a complete number string
• Use --psm 10 (treats the image as a single character) if you’re processing individual digits one by one
• For some cases, --psm 6 (assumes a single uniform block of text) can also yield better results than the raw line mode

2. Switch to the LSTM-Only Engine #

You’re using --oem 3 (hybrid engine), but Tesseract’s LSTM engine (available in v4+) is generally more accurate for printed text and digits. Try updating your config to use --oem 1 instead:

config = "--psm 8 --oem 1 -c tessedit_char_whitelist=0123456789"

3. Refine Your Preprocessing Steps #

Even if your images look good, small tweaks can make a big difference for Tesseract:

• Adaptive Thresholding: Instead of a global threshold, use cv2.adaptiveThreshold() to handle any subtle lighting inconsistencies that might be messing with edge detection
• Noise Reduction: Apply a median blur (cv2.medianBlur()) or Gaussian blur (cv2.GaussianBlur()) to eliminate tiny speckles that could confuse the OCR engine
• Morphological Operations: Use erosion/dilation (cv2.erode()/cv2.dilate()) to sharpen digit edges or remove small artifacts around the numbers

4. Train a Custom Digit Model #

Tesseract’s default training data covers a wide range of fonts and characters, but it might not be optimized for your specific digit style (e.g., segmented display digits, bold/condensed fonts). You can train a custom LSTM model focused solely on 0-9 using Tesseract’s tesstrain tools—this often leads to dramatic accuracy improvements for specialized use cases.

5. Verify Image DPI #

Tesseract performs best with images at 300 DPI. If your source images are low-resolution, even upscaling might not replicate the clarity of a native high-DPI image. Check your image’s DPI and ensure it meets this threshold if possible.

Give these steps a try—start with adjusting the PSM and OEM settings first, since those are quick wins, then move on to refining preprocessing or training a custom model if needed.

内容的提问来源于stack exchange，提问作者Dynamicnotion