Optical Character Recognition, Using K-Nearest Neighbors
This addresses the open problem of OCR for handwritten text, but it is incremental as it applies an existing method (KNN) to this domain.
The paper tackles optical character recognition (OCR) for handwritten text by proposing an approach using the K-nearest neighbors algorithm, achieving an accuracy of over 90% with short training and run times.
The problem of optical character recognition, OCR, has been widely discussed in the literature. Having a hand-written text, the program aims at recognizing the text. Even though there are several approaches to this issue, it is still an open problem. In this paper we would like to propose an approach that uses K-nearest neighbors algorithm, and has the accuracy of more than 90%. The training and run time is also very short.