Hey there! As someone who’s worked through similar Tesseract OCR hurdles, I can tell you that preprocessing is absolutely critical to fixing both your accuracy inconsistency and table recognition problems. Tesseract performs best when fed clean, well-formatted images—let’s break down exactly what you can do:

First: Why Your Accuracy Fluctuates #

The 80% → 2% swing is almost certainly due to inconsistent image quality or missing preprocessing steps. Tesseract is sensitive to factors like lighting, tilt, noise, and contrast, even in the "same" image (e.g., slight variations in capture angle or exposure). Here’s how to stabilize it:

Key Preprocessing Steps for Consistent Accuracy #

• Standardize Resolution: Tesseract works best with images around 300 DPI. Scale your input images to this resolution (adjust based on your source, but avoid going below 200 DPI). For Android, you can use Bitmap.createScaledBitmap() to resize while preserving aspect ratio.
• Noise Reduction: Random speckles or grain can throw off Tesseract. Use Gaussian blur (GaussianBlur() in OpenCV) or median blur (medianBlur()) to smooth out noise without blurring text edges too much.
• Adaptive Binarization: Convert your image to black-and-white using adaptive thresholding instead of a global threshold. This handles uneven lighting (a common cause of accuracy drops) by calculating thresholds for small local regions. Try OpenCV’s adaptiveThreshold() with parameters like ADAPTIVE_THRESH_GAUSSIAN_C and THRESH_BINARY_INV.
• Deskewing (Tilt Correction): Even a small tilt can drastically reduce accuracy. Detect the angle of text lines using Hough Line Transform, then rotate the image to straighten it. Use OpenCV’s getRotationMatrix2D() and warpAffine() to apply the correction.

Fixing Table Recognition (No More Garbled Text) #

Tables are tricky because Tesseract often confuses grid lines with text, or fails to parse cell boundaries. Preprocessing can isolate cell content and eliminate distractions:

Preprocessing for Tables #

• Remove Table Lines: First, identify and erase horizontal/vertical grid lines. Steps to do this:
  1. Binarize the image (use the adaptive method above).
  2. Use morphological operations (like erode() followed by dilate()) to highlight thickened lines.
  3. Find contours of the lines, then fill those regions with the background color to erase them.
• Cell Segmentation: If line removal isn’t enough, crop each table cell individually and run OCR on each cell. To do this:
  1. Detect horizontal and vertical lines to map the table’s grid structure.
  2. Calculate the coordinates of each cell based on the grid lines.
  3. Crop each cell from the original image and process them separately.
• Tweak Tesseract Parameters: Use page segmentation mode (--psm) optimized for structured text. For tables, try --psm 4 (assumes a single column of text, but works well for cell content) or --psm 6 (assumes a single uniform block of text). You can set these parameters when initializing your Tesseract instance.

Extra Tips for Newbies #

• Test Iteratively: Add one preprocessing step at a time and test accuracy to see which ones make the biggest difference for your specific images.
• Use High-Quality Training Data: Make sure you’re using the latest, full language packs for Tesseract 4 (e.g., if you’re working with English, use eng.traineddata from the official Tesseract repo). If your images have rare fonts or symbols, consider training a custom small model later.
• Debug with Visuals: Save the preprocessed images each step so you can see where things might be going wrong (e.g., a binarization step that’s wiping out text, or lines that aren’t being fully removed).

Start with these steps, and you’ll notice a huge improvement in both consistency and table readability. Don’t worry about getting perfect parameters right away—tweaking blur sizes, threshold values, or morphological kernel sizes will go a long way!

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