  Determining the readability of a text is the first step to its
simplification. In this paper, we present a readability analysis tool capable
of analyzing text written in the Bengali language to provide in-depth
information on its readability and complexity. Despite being the 7th most
spoken language in the world with 230 million native speakers, Bengali suffers
from a lack of fundamental resources for natural language processing.
Readability related research of the Bengali language so far can be considered
to be narrow and sometimes faulty due to the lack of resources. Therefore, we
correctly adopt document-level readability formulas traditionally used for U.S.
based education system to the Bengali language with a proper age-to-age
comparison. Due to the unavailability of large-scale human-annotated corpora,
we further divide the document-level task into sentence-level and experiment
with neural architectures, which will serve as a baseline for the future works
of Bengali readability prediction. During the process, we present several
human-annotated corpora and dictionaries such as a document-level dataset
comprising 618 documents with 12 different grade levels, a large-scale
sentence-level dataset comprising more than 96K sentences with simple and
complex labels, a consonant conjunct count algorithm and a corpus of 341 words
to validate the effectiveness of the algorithm, a list of 3,396 easy words, and
an updated pronunciation dictionary with more than 67K words. These resources
can be useful for several other tasks of this low-resource language. We make
our Code & Dataset publicly available at
https://github.com/tafseer-nayeem/BengaliReadability} for reproduciblity.
