Freeman code based online handwritten character recognition for Malayalam using backpropagation neural networks",

ABSTRACT Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry in India. It is written mostly in clockwise direction and consists of loops and curves. The method aims at training a simple neural network with three layers using backpropagation algorithm. Freeman codes are used to represent each character as feature vector. These feature vectors act as inputs to the network during the training and testing phases of the neural network. The output is the character expressed in the Unicode format

Automatic Summarization of Malayalam Documents using Text Extraction Methods

Text summarization is a technique for reducing lengthy passages of text into smaller portions. The goal is to develop a logical and fluent summary that only includes the document's major ideas. It's a important task in Natural Language Processing (NLP) that undoubtedly has a significant impact on synthesization of lengthy documents. With the rise of the digital documentation and publication, devoting time to thoughtfully read an article, document, or book in order to determine its relevance is no longer an option, especially given time constraints. In machine learning and NLP, automatic text summarization is a generic problem. A comparison of text summarization applying the Term Frequency ~ Inverse Document Frequency, Latent Semantic Analysis and Text Rank algorithms for the Malayalam language is presented in this research paper.</jats:p