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

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TransDocAnalyser: A Framework for?Semi-structured Offline Handwritten Documents Analysis with?an?App their inability to localize and label form fields with domain-specific semantics. Existing techniques for semi-structured document analysis have primarily used datasets comprising invoices, purchase orders, receipts, and identity-card documents for benchmarking. In this work, we build the first sem

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Psa617

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Character Queries: A Transformer-Based Approach to?On-line Handwritten Character Segmentationocate relevant positions during the recognition process, it is typically insufficient to produce a precise segmentation. Decoupling the segmentation from the recognition unlocks the potential to further utilize the result of the recognition. We specifically focus on the scenario where the transcript

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Relative Position Embedding Asymmetric Siamese Network for?Offline Handwritten Mathematical Expressio its recursive pattern, the problem of gradient disappearance or gradient explosion also exists for RNN, which makes them inefficient in processing long HME sequences. In order to solve above problems, this paper proposes a Transformer-based encoder-decoder model consisting of an asymmetric siamese

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Semantic Graph Representation Learning for?Handwritten Mathematical Expression Recognitione interactions between different symbols, which may fail when faced similar symbols. To alleviate this issue, we propose a simple but efficient method to enhance semantic interaction learning (SIL). Specifically, we firstly construct a semantic graph based on the statistical symbol co-occurrence pro

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An Encoder-Decoder Method with?Position-Aware for?Printed Mathematical Expression Recognitionep learning have been proposed to solve this task. However, the positional relationship between mathematical symbols is often ignored or represented insufficient, leading to the loss of structural features of mathematical formulas. To overcome this challenge, we propose a position-aware encoder-deco

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