Browsing by Subject "Transformers,"
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Item INFORMATION EXTRACTION FROM STRUCTURED DOCUMENT(I.O.E. Pulchowk Campus, 2023-04-30) KANU, AAYUSH SHAH; POKHREL, ADITHYA; BASHYAL, BISHAL; SHARMA, JANAKThis project proposes the use of the LayoutLMv2 model, a deep learning model, for information extraction from form-like documents. Specifically, the IRS 990 tax form was used as the dataset for testing and optimization. The information extraction process from form-like documents can be challenging due to the complex layout analysis and text recognition required to identify fields and corresponding values. The proposed model, LayoutLMv2, has demonstrated its effectiveness in these tasks, making it a promising solution for information extraction from form-like documents. The project resulted in the development of a web application and annotation tools that provide users with a user-friendly interface to upload documents and extract relevant information accurately and efficiently. The annotation tool enables users to label data and train custom models, while the web application streamlines document processing for businesses and organizations.Item TEXT SUMMARIZATION USING LSA WITH TRANSFORMERS(I.O.E. Pulchowk Campus, 2023-03) NEPAL, ABHAY; TRIPATHI, DIPESH; ADHIKARI, GOKARNA; DHAKAL, KSHITIZThis project endeavors to present an implementation of a text summarization method employing the amalgamation of Latent Semantic Analysis (LSA) with Transformers. The primary objective of the proposed approach is to create a brief summary of an input text while retaining its fundamental meaning. The summarization model is assessed through two metrics, namely BLEU scores and ROUGE scores, which are utilized to gauge the model’s efficacy in generating a succinct and accurate summary. The project comprises several steps, including text preprocessing, feature extraction using LSA, and summary generation using Transformers. The resulting summary is evaluated by comparing it against a reference summary, and the quality of the summary is measured by the BLEU metric and ROUGE scores. The evaluation results reveal that the proposed approach yields high scores on both metrics, indicating its effectiveness in generating precise and concise summaries. Moreover, the project incorporates an analysis of the impact of various parameters on the performance of the summarization model, thereby providing valuable insights into the optimal parameter