This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024.The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions.The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more.

We introduce the Instruction Document Visual Question Answering (iDocVQA) dataset and the Large Language Document (LLaDoc) model, for training Language-Vision (LV) models for document analysis and predictions on document images, respectively. Usually, deep neural networks for the DocVQA task are trained on datasets lacking instructions. We show that using instruction-following datasets improves performance. We compare performance across document-related datasets using the recent state-of-the-art (SotA) Large Language and Vision Assistant (LLaVA)1.5 as the base model. We also evaluate the performance of the derived models for object hallucination using the Polling-based Object Probing Evaluation (POPE) dataset. The results show that instruction-tuning performance ranges from 11x to 32x of zero-shot performance and from 0.1% to 4.2% over non-instruction (traditional task) finetuning. Despite the gains, these still fall short of human performance (94.36%), implying there’s much room for improvement.

Open-set text recognition, which aims to address both novel characters and previously seen ones, is one of the rising subtopics in the text recognition field. However, the current open-set text recognition solutions only focuses on horizontal text, which fail to model the real-life challenges posed by the variety of writing directions in real-world scene text. Multi-orientation text recognition, in general, faces challenges from diverse image aspect ratios, significant imbalance in data amount, and domain gaps between orientations. In this work, we first propose a Multi-Oriented Open-Set Text Recognition task (MOOSTR) to model the challenges of both novel characters and writing direction variety. We then propose a Multi-Orientation Sharing Experts (MOoSE) framework as a strong baseline solution. MOoSE uses a mixture-of-experts scheme to alleviate the domain gaps between orientations, while exploiting common structural knowledge among experts to alleviate the data scarcity that some experts face. The proposed MOoSE framework is validated by ablative experiments, and also tested for feasibility on an existing open-set text recognition benchmark. Code, models, and documents are available at: https://github.com/lancercat/Moose/