Character Spotting Using Machine Learning Techniques
This addresses the problem of text segmentation in unaligned, degraded documents for document analysis applications, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared Support Vector Machines, K-Nearest Neighbor, and an Encoder Network for character spotting in degraded document images, but did not report specific performance numbers or results.
This work presents a comparison of machine learning algorithms that are implemented to segment the characters of text presented as an image. The algorithms are designed to work on degraded documents with text that is not aligned in an organized fashion. The paper investigates the use of Support Vector Machines, K-Nearest Neighbor algorithm and an Encoder Network to perform the operation of character spotting. Character Spotting involves extracting potential characters from a stream of text by selecting regions bound by white space.