  We introduce a new dataset for offline Handwritten Text Recognition (HTR)
from images of Bangla scripts comprising words, lines, and document-level
annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant
to act as ground truth texts. These texts were subsequently used to generate
the annotations that were filled out by people with their handwriting. Our
dataset includes 788 images of handwritten pages produced by approximately 150
different writers. It can be adopted as a basis for various handwriting
classification tasks such as end-to-end document recognition, word-spotting,
word or line segmentation, and so on. We also propose a scheme to segment
Bangla handwritten document images into corresponding lines in an unsupervised
manner. Our line segmentation approach takes care of the variability involved
in different writing styles, accurately segmenting complex handwritten text
lines of curvilinear nature. Along with a bunch of pre-processing and
morphological operations, both Hough line and circle transforms were employed
to distinguish different linear components. In order to arrange those
components into their corresponding lines, we followed an unsupervised
clustering approach. The average success rate of our segmentation technique is
81.57% in terms of FM metrics (similar to F-measure) with a mean Average
Precision (mAP) of 0.547.
