Image Binarization for OCR

In the modern digital era, the ability to automatically extract and interpret text from images is foundational for countless applications. From digitizing books and historical manuscripts to enabling automated license plate recognition and improving accessibility tools for visually impaired individuals, Optical Character Recognition (OCR) stands at the core of many technological solutions.

However, OCR systems face a significant challenge: real-world images are often noisy, blurred, unevenly illuminated, or distorted. Before a machine can accurately recognize text, it must first “see” it in a simplified form that eliminates distractions. This is where image binarization comes in.

Image binarization is the process of converting a grayscale or color image into a binary image consisting of only two pixel values — typically black (0) and white (255). Though simple in concept, it plays a pivotal role in ensuring OCR systems can accurately extract characters from scanned images or photos. Without effective binarization, OCR algorithms often fail to distinguish text from background noise.

Our binarizer works across multiple image formats, including: bmp, cut, dcm, dds, emf, exr, fax, g3, gif, hdr, heic, heif, ico, iff, j2c, j2k, jfif, jng, jp2, jpe, jpeg, jpg, koa, mng, pbm, pcd, pcx, pfm, pgm, pict, png, ppm, psd, ras, raw, sgi, svg, tga, tiff, wbmp, webp, wmf, wsq, xbm, xpm

Binarization isn’t just one method—it’s a field rich with algorithms. Different algorithms influence the quality of output. Each has strengths and weaknesses depending on the document type and imaging conditions. Our binarizer supports following thresholding methods:

1. Global Thresholding: Linear, Otsu

➤ A single threshold is applied across the entire image

✔️ Advantages: Simple, fast, widely used.

❌ Disadvantages: Fails with uneven lighting or complex backgrounds.

2. Local (Adaptive) Thresholding: Ordered Dither, Burke, Stucki, Floyd–Steinberg, Sauvola

➤ Threshold values vary across image regions depending on local intensity statistics.

✔️ Advantages: Handles uneven illumination.

❌ Disadvantages: More computationally expensive.

By combining with an interactive viewer. Our tool allows:

Threshold Adjustment (interactive): Allow dynamic re-binarization by changing threshold sliders.
Zooming: Users can inspect fine-grained details.
Panning: Explore different areas of the image.

Binarized images can be saved in different image file formats: bmp, gif, jpeg, pdf, png, tiff, webp