INTELLIDOC - An Adaptive Transformer-Powered Pipeline For Intelligent Document Processing And Entity Extraction

Efficient and accurate processing of unstructured document data is crucial for legal, enterprise, and academic applications, where vast amounts of textual information must be extracted, summarized, and analyzed. Traditional Optical Character Recognition (OCR) and Named Entity Recognition (NER) methods often face challenges in handling handwritten text, scanned documents, and complex legal structures, leading to data loss and misclassification. To address these limitations, we propose IntelliDoc, an adaptive, transformer-powered document processing pipeline designed to enhance accuracy, efficiency, and contextual understanding of document intelligence. IntelliDoc employs a hybridized multi-stage pipeline that integrates an adaptive OCR layer, which dynamically adjusts to different document characteristics, ensuring high extraction accuracy for diverse document types. Experimental evaluations on a benchmark dataset comprising legal, financial, and administrative documents demonstrate that IntelliDoc achieves an OCR accuracy of 98.2%, NER precision of 94.7%, and a summarization coherence score of 91.5%, significantly outperforming conventional document processing frameworks. Additionally, the parallel architecture reduces processing time by 35% compared to sequential models, making IntelliDoc suitable for real-time applications. Future work will explore integrating domain-specific large language models to further enhance interpretability and accuracy across specialized document categories.