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.

该文章介绍了一个新的离线手写文本识别数据集，集中在孟加拉文字的图像上，该数据集包括 788 张手写页面图像和一个用于无监督聚类线分割的方案。该数据集可以用于各种手写文档识别、词识别、词或行分割等任务。

BN-HTRd: 一份用于离线孟加拉文手写文本识别（HTR）和行分割的基准数据集

BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation

Offline handwriting recognition systems require cropped text line images for
both training and recognition. On the one hand, the annotation of position and
transcript at line level is costly to obtain. On the other hand, automatic line
segmentation algorithms are prone to errors, compromising the subsequent
recognition. In this paper, we propose a modification of the popular and
efficient multi-dimensional long short-term memory recurrent neural networks
(MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. More
particularly, we replace the collapse layer transforming the two-dimensional
representation into a sequence of predictions by a recurrent version which can
recognize one line at a time. In the proposed model, a neural network performs
a kind of implicit line segmentation by computing attention weights on the
image representation. The experiments on paragraphs of Rimes and IAM database
yield results that are competitive with those of networks trained at line
level, and constitute a significant step towards end-to-end transcription of
full documents.

本文提出了一种针对离线手写段落文本识别的神经网络模型，利用注意力权重计算图像表示，实现了一种隐式行分割的可训练端到端模型，试验结果表明其性能与传统基于线段的模型竞争力相当，具有将全文档转录实现的潜力。