  Script identification plays a vital role in applications that involve
handwriting and document analysis within a multi-script and multi-lingual
environment. Moreover, it exhibits a profound connection with human cognition.
This paper provides a new database for benchmarking script identification
algorithms, which contains both printed and handwritten documents collected
from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati,
Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil,
Telugu, and Thai. The dataset consists of 1,135 documents scanned from local
newspaper and handwritten letters as well as notes from different native
writers. Further, these documents are segmented into lines and words,
comprising a total of 13,979 and 86,655 lines and words, respectively, in the
dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning
methods. The benchmark includes results at the document, line, and word levels
with printed and handwritten documents. Results of script identification
independent of the document/line/word level and independent of the
printed/handwritten letters are also given. The new multi-lingual database is
expected to create new script identifiers, present various challenges,
including identifying handwritten and printed samples and serve as a foundation
for future research in script identification based on the reported results of
the three benchmarks.
