  Handwritten word recognition and spotting of low-resource scripts are
difficult as sufficient training data is not available and it is often
expensive for collecting data of such scripts. This paper presents a novel
cross language platform for handwritten word recognition and spotting for such
low-resource scripts where training is performed with a sufficiently large
dataset of an available script (considered as source script) and testing is
done on other scripts (considered as target script). Training with one source
script and testing with another script to have a reasonable result is not easy
in handwriting domain due to the complex nature of handwriting variability
among scripts. Also it is difficult in mapping between source and target
characters when they appear in cursive word images. The proposed Indic cross
language framework exploits a large resource of dataset for training and uses
it for recognizing and spotting text of other target scripts where sufficient
amount of training data is not available. Since, Indic scripts are mostly
written in 3 zones, namely, upper, middle and lower, we employ zone-wise
character (or component) mapping for efficient learning purpose. The
performance of our cross-language framework depends on the extent of similarity
between the source and target scripts. Hence, we devise an entropy based script
similarity score using source to target character mapping that will provide a
feasibility of cross language transcription. We have tested our approach in
three Indic scripts, namely, Bangla, Devanagari and Gurumukhi, and the
corresponding results are reported.
