Tesseract Library and Image Processing to Detect Texts

Extracting text from images is one of those problems that sounds simple until you actually try it. Reading receipts, bills, documents -- the client uploads a photo and you need to turn pixels into usable text.

I spent some time experimenting with Tesseract, an open-source OCR library available on GitHub. I built a demo to test it out.

The API is surprisingly simple:

Tesseract.recognize(url.preview, "eng", {        
   logger: m => console.log(m)      
})

Feed it an image, specify the language, and it returns the detected text. The logger callback gives you progress updates, which is useful for showing a loading state in your UI.

The Trade-offs

Tesseract works well on clean, high-contrast images with standard fonts. Hand-written text, low-resolution photos, or unusual layouts will give you mediocre results. You can improve accuracy with image preprocessing -- contrast adjustment, deskewing, noise reduction -- but that adds complexity.

For production use, consider pairing it with image processing steps before passing the image to Tesseract. The raw library alone is a starting point, not a complete solution.

You can also take this further by combining OCR with Natural Language Processing to understand the extracted text. I wrote about NLP here.