Video captioning in Nepali, a language written in the Devanagari script,
presents a unique challenge due to the lack of existing academic work in this
domain. This work develops a novel encoder-decoder paradigm for Nepali video
captioning to tackle this difficulty. LSTM and GRU sequence-to-sequence models
are used in the model to produce related textual descriptions based on features
retrieved from video frames using CNNs. Using Google Translate and manual
post-editing, a Nepali video captioning dataset is generated from the Microsoft
Research Video Description Corpus (MSVD) dataset created using Google
Translate, and manual post-editing work. The efficacy of the model for
Devanagari-scripted video captioning is demonstrated by BLEU, METOR, and ROUGE
measures, which are used to assess its performance.

本研究论文利用 LSTM 和 GRU 模型开发了一种新颖的编码器 - 解码器范例，用于尼泊尔视频字幕生成，利用 Devanagari 文字进行相关描述，并通过 BLEU、METOR 和 ROUGE 等指标评估其性能。

基于注意力机制的编码解码模型用于尼泊尔语视频字幕生成 (2023)

Attention Based Encoder Decoder Model for Video Captioning in Nepali  (2023)

The monolingual Hindi BERT models currently available on the model hub do not
perform better than the multi-lingual models on downstream tasks. We present
L3Cube-HindBERT, a Hindi BERT model pre-trained on Hindi monolingual corpus.
Further, since Indic languages, Hindi and Marathi share the Devanagari script,
we train a single model for both languages. We release DevBERT, a Devanagari
BERT model trained on both Marathi and Hindi monolingual datasets. We evaluate
these models on downstream Hindi and Marathi text classification and named
entity recognition tasks. The HindBERT and DevBERT-based models show
significant improvements over multi-lingual MuRIL, IndicBERT, and XLM-R. Based
on these observations we also release monolingual BERT models for other Indic
languages Kannada, Telugu, Malayalam, Tamil, Gujarati, Assamese, Odia, Bengali,
and Punjabi. These models are shared at this https URL .

通过 Hindi BERT 和 Devanagari BERT 模型，我们实现了 Hindi 和 Marathi 文本分类和实体识别任务的重大改进，并在其基础上为其他 Indic 语言发布了单语 BERT 模型。

L3Cube-HindBERT 和 DevBERT：针对基于天城文的印地语和马拉提语的预训练 BERT Transformer 模型

L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages

This paper describes a new feature set, called the extended directional
features (EDF) for use in the recognition of online handwritten strokes. We use
EDF specifically to recognize strokes that form a basis for producing
Devanagari script, which is the most widely used Indian language script. It
should be noted that stroke recognition in handwritten script is equivalent to
phoneme recognition in speech signals and is generally very poor and of the
order of 20% for singing voice. Experiments are conducted for the automatic
recognition of isolated handwritten strokes. Initially we describe the proposed
feature set, namely EDF and then show how this feature can be effectively
utilized for writer independent script recognition through stroke recognition.
Experimental results show that the extended directional feature set performs
well with about 65+% stroke level recognition accuracy for writer independent
data set.

本文描述了一种新的特征集（称为扩展方向特征），用于识别在线手写笔画，特别是用于识别印度最广泛使用的语言文字 Devanagari script。我们通过扩展方向特征集有效地实现了基于笔画识别的独立于作者的脚本识别，实验结果表明，扩展方向特征集对于独立于作者的数据集具有约 65％以上的笔画识别精度。