  Handwriting recognition remains challenging for some of the most spoken
languages, like Bangla, due to the complexity of line and word segmentation
brought by the curvilinear nature of writing and lack of quality datasets. This
paper solves the segmentation problem by introducing a state-of-the-art method
(BN-DRISHTI) that combines a deep learning-based object detection framework
(YOLO) with Hough and Affine transformation for skew correction. However,
training deep learning models requires a massive amount of data. Thus, we also
present an extended version of the BN-HTRd dataset comprising 786 full-page
handwritten Bangla document images, line and word-level annotation for
segmentation, and corresponding ground truths for word recognition. Evaluation
on the test portion of our dataset resulted in an F-score of 99.97% for line
and 98% for word segmentation. For comparative analysis, we used three external
Bangla handwritten datasets, namely BanglaWriting, WBSUBNdb_text, and ICDAR
2013, where our system outperformed by a significant margin, further justifying
the performance of our approach on completely unseen samples.
