Evaluation of OCR algorithms for images with different spatial resolutions and noises

Abstract

Various shaped-based image invariants are popular algorithms used in optical character recognitions (OCR), 3D object recognitions, and pattern recognitions. The shape-based image invariants can be divided into two different categories: boundary based image invariants such as Fourier descriptors and chain code; region-based image invariants including various moment-based invariants such as Hu's seven moment invariants and Zernike moments. This thesis introduced and evaluated different shape-based image invariants from the perspective of their invariance property to image transformations including scaling, translation, rotation and different image spatial resolutions. The influence caused by salt and pepper noises of different intensities is also analyzed. The image reconstruction ability of Zernike moments is implemented and discussed as well. An OCR engine is implemented with MATLAB scripts to perform the image feature extraction and image recognition. The OCR engine is also used in the overall performance evaluation of Fourier descriptors and Hu's seven moment invariants.