This is a monorepo for our code.
Computer vision is a branch of artificial intelligence that involves applications of mathematical methods and computers for machine learning from digital images and videos. Computer vision finds key applications for process automation like using optical character recognition (OCR) for automated check clearing, digitizing text and image records for online databases, automated analysis of surveillance camera videos for security, automated reading of text from car license plates in a parking lot, image compression for online storage, image identification and search, etc. We digitized hand written character images into arrays and applied transformations to vectorize data for dimensional reduction. We applied vector and scalar projections of these vectors for optical character recognition. We applied linear-algebra-based algorithms and wrote codes using python scientific libraries for optical character recognition and image compression. We characterized color and grayscale images as arrays and used singular value decompositions and principal component analysis for grayscale and color image compression studies. We carried out singular value decompositions and principal component analysis using Python Spyder and Jupyterbook for optical character recognition and image compression. These studies illustrate how mathematical transformations and data reduction methods can be used for optical character recognition, image compression, identification and encryption. This project elucidates the key role of mathematical modeling for computer vision applications.
- Tux Image: Drawn and Designed by Larry Ewing ([email protected]) with GIMP.
- Konqi Image: Creative Commons Attribution-Share Alike 4.0 International. Redesign of Konqi by Tyson Tan.