HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System

In this paper, we introduce a novel neural architecture that can judge the result of extracted structured information from documents Measuring the confidence of AI models is critical for safely deploying provided by the information extracting neural networks AI in real-world industrial systems. One important application (hereafter referred to as the IE Networks). Our architecture is hybrid, of confidence measurement is information extraction from scanned consisting of two models, which are a Multi-modal Conformal documents. However, there exists no solution to provide reliable Predictor (MCP) and an Variational Cluster-oriented Anomaly Detector confidence score for current state-of-the-art deep-learning-based (VCAD). The former aims to combine the neural signals from information extractors. In this paper, we propose a complete and 3 main stages of information extraction processes including textbox novel architecture to measure confidence of current deep learning localization, OCR, and key-value recognition to predict the models in document information extraction task. Our architecture confidence level for each extracted key-value output. The later computes consists of a Multi-modal Conformal Predictor and a Variational anomaly scores for the raw input document image, providing Cluster-oriented Anomaly Detector, trained to faithfully estimate the MCP with additional features to produce better confidence estimation.