  India is a multi-lingual country where Roman script is often used alongside
different Indic scripts in a text document. To develop a script specific
handwritten Optical Character Recognition (OCR) system, it is therefore
necessary to identify the scripts of handwritten text correctly. In this paper,
we present a system, which automatically separates the scripts of handwritten
words from a document, written in Bangla or Devanagri mixed with Roman scripts.
In this script separation technique, we first, extract the text lines and words
from document pages using a script independent Neighboring Component Analysis
technique. Then we have designed a Multi Layer Perceptron (MLP) based
classifier for script separation, trained with 8 different wordlevel holistic
features. Two equal sized datasets, one with Bangla and Roman scripts and the
other with Devanagri and Roman scripts, are prepared for the system evaluation.
On respective independent text samples, word-level script identification
accuracies of 99.29% and 98.43% are achieved.
