Aug 2026· International Journal of Innovative Science and Research Technology· 0 citations· 28 references
TL;DR
The developed application is an example of the effective use of Artificial Intelligence in processing documents in regional languages and lays the groundwork for creating automated systems for document management.
Abstract
The fast expansion of digital data has resulted in high demand for smart systems which can quickly obtain
compact and meaningful data from lengthy documents. Even though automation in text summarization has seen
significant developments in well-resourced languages, automated summarization of Kannada is still rare due to the
intricacies of the script of the language, poor computational power, and massive information available in printed and
scanned form. A web-based framework for automated summarization of Kannada documents is introduced in this paper,
utilizing AI by developing a processing platform that integrates OCR and NLP. This technique works not only with
originally typed text in Kannada but also with scanned documents. Scanned documents are converted to editable Unicode
by using Tesseract OCR engine before performing the language-specific NLP tasks that include normalization,
tokenization, sentence splitting, stopword removal, and extraction of summaries. Using technologies such as Python, Flask,
OpenCV, Tesseract OCR, and relational database management, the developed application can ensure secure
authentication of users, management of documents, and visualization of summaries using an interactive web interface. The
successful tests indicate that OCR and NLP technologies have been integrated into the process of performing various tasks
related to the examination of documents written in the Kannada language. The modular architecture of the project
enables applying transformer-based summaries, document processing in many languages, OCR of handwritten texts
written in the Kannada language, and various technologies for running applications in the cloud in the future. Thus, the
developed application is an example of the effective use of Artificial Intelligence in processing documents in regional
languages and lays the groundwork for creating automated systems for document management.
With the ever-increasing digital information, there is a high demand for automatic text summarization systems to produce meaningful summaries from longer documents. Despite significant efforts in automatic text summarization for high resource languages like English, research on automatic text summarization in Gujarati is limited because of the lack of linguistic resources, a lack of annotated datasets, and the difficult grammar of the Gujarati language. Previous multilingual transformer models like mBART, IndicBART and mT5 have shown promising results; however, they tend to produce grammatically incorrect, repetitive and incongruent summaries for Gujarati documents. In this study, we present a Gujarati specific abstractive text summarization system that leverages an improved multilingual mT5 model with a linguistic preprocessing step on the input text, morphological normalization, named entity preservation and coverage-aware decoding step. The proposed system takes as input any paragraph or article of Gujarati text or any document of large text, and produces one or two sentence summaries which are semantically equivalent to the original document and contain the information. The proposed framework would achieve better quality of summarization, grammatical correctness, semantic consistency, computational efficiency, and solve the issues with low-resource languages from India. To prove the superiority of the proposed model over the existing multilingual summarization models, automatic evaluation metrics such as ROUGE, BLEU, BERTScore will be employed in addition to human evaluation.
Ankit Dhansukhbhai Prajapati, Rakesh Kumar Bhujade· International journal of com...· 0 citations
The design realization and evaluation of an Automated Summarization Tool (AST) is presented which is a document intelligence platform based on google gemini 2.5 flash that outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points and is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.
K. Kumar, A. Amandeep, Dharmender Kumar et al.· International Journal of Inn...· 0 citations
India is home to an incredible number of languages which leads to the production of substantial amounts of news articles provided in their regional languages including Telugu, Tamil, Hindi, Bengali, Kannada, and Malayalam. Unfortunately, current systems for processing this data do so independently, rather than as part of a comprehensive framework utilizing all the necessary components in one queue of processing pipelines; OCR extraction, Translation, Summarization, and Bias Detection must all be completed one at a time, and do not allow for seamless data passing between functions. In this paper, we will present Bharat Sum, a multilingual news summarization and bias detection system that incorporates OCR capabilities into five different types of processing stages; OCR Text Extraction using Tesseract, Automatic Language Detection using LangDetect, Topic Segmented Abstractive Summarisation using mT5, Translation to English using mBART, and Sentiment based Bias Classification using DistilBERT - all accessible through a single scalable architecture running on commodity hardware and implemented via Streamlit. Our testing involved 150 news articles covering each of the five languages named above. The results achieved were as follows; OCR extraction accuracy of 89.7%, Language Detection Accuracy of 94.2%, Summarisation Quality (ROUGE-L F1) of 92.1% Translation consistency of 91.4% and Sentiment Classification Accuracy of 88.6%. The average end-to-end processing time was between 10 and 16 seconds. Our analysis of Bharat Sum has revealed that it significantly outperforms previous single function systems by providing an Integrated, Real-Time Multilingual Processing capability which currently does not exist in this context. Bharat Sum has the potential to address significant gaps in the research literature regarding the analysis of Integrated Multilingual Media, and will likely serve as an economically viable solution for organisations conducting Digital Journalism, Media Monitoring, or Accessing Multilingual Information.
Farooq Sunar Mohammad, E.Sneha, B.Kavya et al.· 2026 7th International Confe...· 0 citations
The growing volume of scientific literature has driven the demand for
automated text summarization systems that are natural and factual. Extractive Text
Summarization methods are factually accurate in meaning; still, they can lead to a summary
that is not cohesive. On the other hand, abstractive summarization systems improve readability
but may introduce factual bias. The paper overcomes these shortcomings by creating a hybrid
text summarization system that combines extractive and abstractive methods to maximize both
quality and factual content.
The framework uses two unsupervised extractive models, HipoRank and PacSum, to
extract important sentences, which are then synthesized with the original input document's
introduction section and subjected to long-document transformer models, PEGASUSX and
LED, to generate abstract-style summaries.
Among the tested combinations, the HipoRank-LED configuration achieved the most balanced performance, with ROUGE-1: 0.440, ROUGE-2: 0.220, and ROUGE-L: 0.410 on the PubMed dataset. This combination occasionally produced summaries with greater abstractiveness than the human-written references.
Various experiments across the ScisummNet, ArXiv, and PubMed datasets show that
hybrid configurations are always better than extractive and abstractive ones. HipoRank-LED is
the most efficient model, with ROUGE-1 = 0.440, ROUGE-2 = 0.220, and ROUGE-L = 0.410
on PubMed.
Results indicate that combining extractive grounding with long-context
transformers improves informativeness and coherence and reduces hallucination errors. The
introduction-guided structured input also provides better global context for summarizing
complex scientific documents.
The findings indicate that the transformer-based abstraction, combined with an
extractive text summarization approach, can be a very useful, scalable, and domain-independent
model for approximating long scientific texts.
Traditional document management systems suffer from inefficiencies in organization, retrieval, and data extraction, often relying on manual entry and rigid field structures that fail to accommodate diverse document types. This project proposes an AI Smart Document Management System with Dynamic Field Extraction that leverages Deep Learning and modern web technologies to address these limitations. The system supports a broad range of document categories including invoices, contracts, medical records, and academic transcripts enabling intelligent, automated extraction of key fields without requiring predefined templates. Optical Character Recognition (OCR) powered by
Tesseract processes uploaded documents, while a Large Language Model (LLM) running through the Groq API performs context-aware, dynamic field extraction and document summarization. All documents are indexed and stored in MongoDB, with vector embeddings enabling semantic search across the repository. Users can perform natural language queries to retrieve relevant documents efficiently, bypassing traditional keyword-based search limitations. The backend is developed using FastAPI for scalable and asynchronous API handling, while the frontend is built with React.js for a modern, responsive user experience. Authentication and session management are secured using JWT tokens. The system further incorporates AI-assisted summarization and an intelligent assistant for document-level querying. By combining OCR, LLM-driven extraction, and semantic retrieval, this solution significantly reduces manual processing overhead and modernizes organizational document workflows.
Keywords:
Smart Document Management, Dynamic Field Extraction, Optical Character Recognition, Large Language Models, Semantic Search, Vector Embeddings
Kata Raju Reddy, Korada Ramya· International Journal of Sci...· 0 citations
This study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches that is affordable, scalable, and suitable for business, education, and research.
Radhika Sharma, Devraj Gautam· Revolutionary Advances in Co...· 0 citations