Is Python OCR Inaccurate? Try These Image Preprocessing Techniques!

When using Python for OCR (Optical Character Recognition), poor image quality — such as blur, skew, or noise — can lead to low recognition accuracy. This article introduces essential image preprocessing techniques to improve OCR performance, along with recommended third-party image enhancement APIs.

✅ 1. Key Image Preprocessing Techniques

1. Adaptive Thresholding to Enhance Contrast

Use adaptive thresholding to handle uneven lighting or background:

import cv2

img = cv2.imread('input.jpg', 0)

binary = cv2.adaptiveThreshold(

    img, 255,

    cv2.ADAPTIVE_THRESH_GAUSSIAN_C,

    cv2.THRESH_BINARY, 11, 2

)

cv2.imwrite('binary.jpg', binary)

1. Denoising and Removing Artifacts

blur = cv2.GaussianBlur(binary, (3, 3), 0)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
denoised = cv2.morphologyEx(blur, cv2.MORPH_OPEN, kernel)
cv2.imwrite('denoised.jpg', denoised)

import numpy as np


coords = cv2.findNonZero(denoised)

angle = cv2.minAreaRect(coords)[-1]

if angle < -45:

    angle = -(90 + angle)

else:

    angle = -angle

(h, w) = denoised.shape[:2]

center = (w // 2, h // 2)

M = cv2.getRotationMatrix2D(center, angle, 1.0)

rotated = cv2.warpAffine(denoised, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

cv2.imwrite('rotated.jpg', rotated)

Bicubic interpolation is recommended to retain text clarity:

resized = cv2.resize(rotated, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)

cv2.imwrite('resized.jpg', resized)

1. Shadow and Uneven Lighting Removal

dilated = cv2.dilate(img, np.ones((7,7), np.uint8))
bg = cv2.medianBlur(dilated, 21)
diff = 255 - cv2.absdiff(img, bg)
norm = cv2.normalize(diff, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
cv2.imwrite('shadow_removed.jpg', norm)

🚀 2. Recommended Image Enhancement APIs

To simplify preprocessing, consider using these high-quality online tools and APIs:

📐 Document Correction

Auto deskew and perspective correction

📄 Virtual Scanner

Scan-like enhancement & background removal

🌫️ Shadow Removal

Fix uneven lighting and shadows

🔍 Image Enhancement

Improve sharpness, contrast, brightness

🌐 OCR + Translation

Extract and translate text automatically

Visit API

🧠 3. Recommended OCR Processing Workflow

1. 📤 Upload original image → Process using API (deskew, denoise, etc.)
2. 📥 Download enhanced image
3. ⚙️ Apply further preprocessing if needed (thresholding, resize, etc.)
4. 🔠 Use OCR engine (Tesseract / PaddleOCR) to extract text
5. 🧹 Post-process output (correct errors, restore layout)

Need a full Python template to automate preprocessing and OCR? Let me know — I can provide a complete script.