Character Spotting Using Machine Learning Techniques

TL;DR

This paper compares machine learning algorithms like SVM, KNN, and Encoder Networks for character spotting in degraded, unaligned document images, focusing on segmenting potential characters based on whitespace boundaries.

Contribution

It introduces a comparative analysis of multiple ML algorithms specifically tailored for character spotting in challenging document images.

Findings

Support Vector Machines effectively identify characters in degraded documents.

K-Nearest Neighbor performs well with unaligned text.

Encoder Networks show promise for complex character segmentation.

Abstract

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.