Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

揉雜

https://doi.org/10.1007/978-1-4615-3296-5ate balanced, diverse, and accurately annotated slide data. We demonstrate SlideCraft’s efficacy in enhancing slide layout analysis algorithms, focusing on its capability to improve dataset quality and object detection performance.

違反

Irritate

tattle

Recurrent Few-Shot Model for?Document Verification to document resolution variability. Moreover, the few-shot approach allow the model to perform well even for unseen class of documents. Preliminary results on the SIDTD and Findit datasets show good performance of this model for this task.

骯臟

SlideCraft: Synthetic Slides Generation for?Robust Slide Analysisate balanced, diverse, and accurately annotated slide data. We demonstrate SlideCraft’s efficacy in enhancing slide layout analysis algorithms, focusing on its capability to improve dataset quality and object detection performance.

擁護(hù)者

Visual Prompt Learning for?Chinese Handwriting Recognitionith the embeddings of previously predicted text to guide the decoding process. Experiments conducted on the SCUT-HCCDoc, SCUT-EPT and CASIA-HWDB Chinese handwriting datasets validate the effectiveness of the proposed methods.

lobster

披肩

A Multiclass Imbalanced Dataset Classification of?Symbols from?Piping and?Instrumentation Diagramsest in developing solutions for processing and analysing these diagrams using wide range of image-processing and computer vision techniques. In this paper, we first, present a new multiclass imbalanced dataset of symbols extracted from Piping and Instrumentation Diagrams (P&IDs). The dataset contain

公共汽車

Weakly Supervised Training for?Hologram Verification in?Identity Documents Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on M

Mindfulness

Multi-task Learning for?License Plate Recognition in?Unconstrained Scenariosstudies have treated license plate detection and recognition as separate tasks, resulting in inefficiencies and error accumulation. To address these challenges, we propose an end-to-end method for license plate detection and recognition using multi-task learning. Firstly, we introduce two parallel b