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This paper introduces a deep learning model tailored for document informationanalysis, emphasizing document classification, entity relation extraction, anddocument visual question answering. The proposed model leveragestransformer-based models to encode all the information present in a documentimage, including textual, visual, and layout information. The model ispre-trained and subsequently fine-tuned for various document image analysistasks. The proposed model incorporates three additional tasks during thepre-training phase, including reading order identification of different layoutsegments in a document image, layout segments categorization as per PubLayNet,and generation of the text sequence within a given layout segment (text block).The model also incorporates a collective pre-training scheme where losses ofall the tasks under consideration, including pre-training and fine-tuning taskswith all datasets, are considered. Additional encoder and decoder blocks areadded to the RoBERTa network to generate results for all tasks. The proposedmodel achieved impressive results across all tasks, with an accuracy of 95.87%on the RVL-CDIP dataset for document classification, F1 scores of 0.9306,0.9804, 0.9794, and 0.8742 on the FUNSD, CORD, SROIE, and Kleister-NDA datasetsrespectively for entity relation extraction, and an ANLS score of 0.8468 on theDocVQA dataset for visual question answering. The results highlight theeffectiveness of the proposed model in understanding and interpreting complexdocument layouts and content, making it a promising tool for document analysistasks.

Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents