Plagiarism means taking another person's work and not giving any credit to
them for it. Plagiarism is one of the most serious problems in academia and
among researchers. Even though there are multiple tools available to detect
plagiarism in a document but most of them are domain-specific and designed to
work in English texts, but plagiarism is not limited to a single language only.
Bengali is the most widely spoken language of Bangladesh and the second most
spoken language in India with 300 million native speakers and 37 million
second-language speakers. Plagiarism detection requires a large corpus for
comparison. Bengali Literature has a history of 1300 years. Hence most Bengali
Literature books are not yet digitalized properly. As there was no such corpus
present for our purpose so we have collected Bengali Literature books from the
National Digital Library of India and with a comprehensive methodology
extracted texts from it and constructed our corpus. Our experimental results
find out average accuracy between 72.10 % - 79.89 % in text extraction using
OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have
built a web application for end-user and successfully tested it for Plagiarism
detection in Bengali texts. In future, we aim to construct a corpus with more
books for more accurate detection.

通过 OCR 技术和 Levenshtein Distance 算法，我们构建了一个能够成功检测孟加拉文文本抄袭的网络应用，并利用国家数字图书馆的文献构建了一个文本语料库以提高准确性。

基于文本相似度的孟加拉语抄袭检测

Plagiarism Detection in the Bengali Language: A Text Similarity-Based Approach

Bengali literature has a rich history of hundreds of years with luminary
figures such as Rabindranath Tagore and Kazi Nazrul Islam. However, analytical
works involving the most recent advancements in NLP have barely scratched the
surface utilizing the enormous volume of the collected works from the writers
of the language. In order to bring attention to the analytical study involving
the works of Bengali writers and spearhead the text generation endeavours in
the style of existing literature, we are introducing RabindraNet, a character
level RNN model with stacked-LSTM layers trained on the works of Rabindranath
Tagore to produce literary works in his style for multiple genres. We created
an extensive dataset as well by compiling the digitized works of Rabindranath
Tagore from authentic online sources and published as open source dataset on
data science platform Kaggle.

使用最新的自然语言处理 (NLP) 技术，通过给 Bengali 文学奠定基础的杰出作家 Rabindranath Tagore 的专业培训，我们创建了一个基于堆叠 LSTM 层的字符级 RNN 模型 RabindraNet，用于在多种流派中生成与 Rabindranath Tagore 的作品风格相似的文学作品，并通过在数据科学平台 Kaggle 上发表开放源代码数据集的形式来创造广泛的数据集。