Read My Finger OCR

Problem

Image-based text extraction can be unreliable when the input varies in quality, layout, or formatting. The challenge was producing dependable results from imperfect input.

Goal

The goal was to create a workflow that could detect and extract meaningful text information with clarity and consistency.

Solution

I applied a document-oriented approach that focuses on image preprocessing, structured extraction, and practical output formatting.

Technologies

Python and computer vision tooling were ideal because they support accurate image handling and text extraction tasks.

My Role

I built the extraction pipeline and shaped how the results were structured and delivered.

Challenges

Noise, imperfect images, and inconsistent formatting made extraction a challenging problem.

How I Solved Them

I emphasized careful preprocessing and output validation, which helped improve reliability and usability.

Results

The project demonstrated practical computer vision skills in a way that is immediately understandable to recruiters and hiring teams.

Lessons Learned

This project highlighted the difference between building a model and building a useful product.

Future Improvements

I would add stronger layout understanding, better confidence scoring, and broader document support.