  Recently, recognition of handwritten Bengali letters and digits have captured
a lot of attention among the researchers of the AI community. In this work, we
propose a Convolutional Neural Network (CNN) based object detection model which
can recognize and evaluate handwritten Bengali mathematical expressions. This
method is able to detect multiple Bengali digits and operators and locate their
positions in the image. With that information, it is able to construct numbers
from series of digits and perform mathematical operations on them. For the
object detection task, the state-of-the-art YOLOv3 algorithm was utilized. For
training and evaluating the model, we have engineered a new dataset 'Hishab'
which is the first Bengali handwritten digits dataset intended for object
detection. The model achieved an overall validation mean average precision
(mAP) of 98.6%. Also, the classification accuracy of the feature extractor
backbone CNN used in our model was tested on two publicly available Bengali
handwritten digits datasets: NumtaDB and CMATERdb. The backbone CNN achieved a
test set accuracy of 99.6252% on NumtaDB and 99.0833% on CMATERdb.
