Paper Title
FUSION-BASED MULTIMODAL RETRIEVAL AND GENERATION FOR COMPLEX HANDWRITTEN DOCUMENT WORKFLOWS

Abstract
The extraction and interpretation of handwritten text from handwritten documents remain one of the frontier tasks for NLP mechanisms and models of computer vision. This study demonstrates the development of a precisely engi- neered framework that employs both LLMs and CV models in performing a more extensive text extraction and interpretation process. It provides a semantic, layout-sensitive reasoning method for text-centric tasks. Some of the key features for displayed approach are advanced data augmentation, feature alignment, ONNX-TensorRT deployment [4][7], and code level reusability and reproducibility. This particularly enhanced and adaptive performance compared to the advanced approaches nowadays. Keywords - Multimodal retrieval, transformer fusion, layout analysis, handwritten OCR, LLM fine-tuning, prefix injection, ONNX deployment, TensorRT, ablation study, document intelligence.