Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

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Interlingua Machine Translationever, the independence of the dictionary on the visual features may lead to incorrect rectification of accurate visual predictions. In this paper, we propose a new dictionary language model leveraging the .cene .mage-.ext .atching(SITM) network, which avoids the drawbacks of the explicit dictionary

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Basic Terminology and Backgroundnd all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training

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Translation Issues in Language and Lawer adverse weather conditions with poor visibility remains challenging. To address this problem, we propose a text image enhancement network that can be embedded into a scene text recognizer in a pluggable manner. This network comprises multiple sets of digital image processing (DIP) units, which ar

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Pitfalls of English as a Contract Languagetion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason o

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Document Analysis and Recognition - ICDAR 2023978-3-031-41731-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentationtion of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable and generalizable approach to identify text reading order with a multi-modal, multi-task graph convolutional network (GCN) runnin

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TDAE: Text Detection with Affinity Areas and Evolution Strategiesare robust to detect text of any shape. However, most previous works focus on word-level detection and neglect the regions between adjacent words, which are helpful when some text instances are very close. In this paper, we propose a novel image feature named affinity area that exploits the area bet