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@article{205300,
        author = {Kamlesh Kale and Gayatri Hegde and Nusrat Parveen and Soham Bait and Aditya Kuashwaha and Anuj Karambalkar},
        title = {Business Documentation Classifier and Analyzer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7211-7218},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205300},
        abstract = {The Business Documentation Classifier and Analyzer powered by AI is an intelligent automation solution aimed at the classification, extraction and analysis of business documentations and financial information contained within various documents like invoice, receipt, report, etc. This document classification uses a combination of OCR i.e., Tesseract, AWS Textract, ML and NLP I.e., LayoutLMv3, spaCy, Regex, to convert unstructured document data into usable insights After extracting this information, it can be stored in a hybrid database model of MongoDB. The dashboard is built using React.js and Tailwind CSS, and visualization of financial analytics is done using Plotly and Chart.js. The backend is built using Node.js, Express.js and Flask to manage all APIs, ML pipelines and logic, and uses Gemini API for intelligent summarization. Finally, this solution is deployed on Vercel and AWS, using CI/CD pipelines to support scalability, reliability and rapid data-driven financial decision-making for the modern business.},
        keywords = {OCR, Tesseract, AWS Textract, Machine Learning (ML), Natural Language Processing (NLP), LayoutLMv3, spaCy, Regex, data extraction, Supabase, SQL, a MongoDB hybrid database, React.js, Tailwind CSS, Plotly, Chart.js, Node.js, Express.js, Flask, Gemini API, summary/document aggregation, and implementation via Vercel using AWS infrastructure and CI/CD pipelines.},
        month = {June},
        }