Great question! I’ve tinkered with Tesseract.js for ID card recognition projects before, so here are some practical, actionable tweaks to boost name recognition quality—even with the 3.04 ported version:

• Prioritize Image Preprocessing
  Camera input is often the biggest bottleneck for OCR accuracy. Clean up the image before feeding it to Tesseract:

  • Grayscale conversion: Strip color data to reduce noise. Use a library like Jimp or native Canvas API: image.grayscale()
  • Deskewing: Correct any tilt in the ID card. You can implement simple edge detection to calculate rotation angle, or use a lightweight image processing tool to straighten the frame.
  • Contrast enhancement: Fix uneven lighting with histogram equalization or contrast adjustments (e.g., Jimp.contrast(0.6)). This makes character edges sharper and easier for Tesseract to parse.
  • ROI Cropping: Crop only the name section of the ID card instead of processing the whole frame. Reducing irrelevant content eliminates distractions and speeds up recognition.

• Tune Tesseract’s Core Parameters
  The 3.04 version has hidden parameters tailored for fixed-format text like ID names:

  • Character whitelist: Restrict recognition to only valid characters (Chinese characters + any rare/special characters you need). Example:
    tesseract.setParameters({ tessedit_char_whitelist: '赵钱孙李...（all relevant Chinese chars + special symbols）' })
    This cuts down on false matches drastically.
  • Single-line mode: Since names are always a single line, set page segmentation mode to 7:
    tesseract.setParameters({ tessedit_pageseg_mode: 7 })
    This tells Tesseract to treat the input as a single line of text, avoiding missegmentation.
  • Length filters: Set minimum and maximum character lengths for names (e.g., 2-8 characters) to filter out nonsensical results.

• Train a Custom Traineddata (For Edge Cases)
  If you’re dealing with frequent rare characters that the default traineddata misses, you can train a custom dataset for the 3.04 port. Collect sample images of those rare characters, label them, and use Tesseract’s training tools to generate a supplementary traineddata file. It’s a bit labor-intensive, but it’s a game-changer for edge cases.

• Multi-Frame Voting
  For camera-based recognition, capture multiple consecutive frames and use a voting system to pick the most consistent result. If 7 out of 10 frames recognize "张三", that’s far more reliable than a single frame’s output. This mitigates temporary blur or glare from the camera feed.

I’ve used these exact methods to get usable, accurate results with the 3.04 Tesseract.js port for ID card names—they should help you hit your target!

内容的提问来源于stack exchange，提问作者tugend