CITlab ARGUS for Arabic Handwriting

원문 초록

In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and dictionary lookup, our ARGUS software completed this task with an error rate of 26.27% in its primary setup.

함께 살펴볼 논문

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arXiv:2501.18116공통 태그 Handwriting Recognition공식 코드 · Papers with Code 등록 2025-01-30

DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification

arXiv:2407.09103공통 태그 Handwriting Recognition공식 코드 · Papers with Code 등록 2024-07-12

DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents

arXiv:2309.03072공통 태그 Handwriting Recognition공식 코드 · Papers with Code 등록 2023-09-06

Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation

arXiv:2307.15071공통 태그 Handwriting Recognition공식 코드 · Papers with Code 등록 2023-07-11

Writer adaptation for offline text recognition: An exploration of neural network-based methods

arXiv:2306.13557공통 태그 Handwriting Recognition공식 코드 · Papers with Code 등록 2023-06-23

FPGA Implementation of Convolutional Neural Network for Real-Time Handwriting Recognition

arXiv:2306.09351공통 태그 Handwriting Recognition공식 코드 · Papers with Code 등록 2023-05-31

BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images

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