DEEP LEARNING-BASED DETECTION AND CLASSIFICATION OF DOCUMENT ELEMENTS USING ROBOFLOW

Abstract

Recent advances in deep learning have enabled significant improvements in document understanding and processing. This paper presents a workflow for detecting and classifying key document elements—paragraphs of text, tables, signatures, and stamps—using deep learning models implemented through the Roboflow platform. We address the unique challenges of document element detection, including complex spatial relationships, overlapping boundaries, and varying formats across different document types. Our methodology leverages Roboflow's comprehensive toolkit for dataset preparation, model training, and deployment, with particular attention to data augmentation techniques specific to document processing. The proposed system achieves robust detection and classification of document elements. Experimental results demonstrate the system's effectiveness in real-world applications. Our findings indicate that the proposed approach offers significant accuracy and efficiency of automated document processing, particularly in scenarios requiring the precise identification and extraction of structured and unstructured document components.