Search Results for author: Kai Ding

Found 13 papers, 6 papers with code

Datasets for Large Language Models: A Comprehensive Survey

1 code implementation28 Feb 2024 Yang Liu, Jiahuan Cao, Chongyu Liu, Kai Ding, Lianwen Jin

Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from 444 datasets, covering 8 language categories and spanning 32 domains.

Language Modelling Large Language Model

UPOCR: Towards Unified Pixel-Level OCR Interface

no code implementations5 Dec 2023 Dezhi Peng, Zhenhua Yang, Jiaxin Zhang, Chongyu Liu, Yongxin Shi, Kai Ding, Fengjun Guo, Lianwen Jin

Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models.

Optical Character Recognition Optical Character Recognition (OCR) +2

DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures

no code implementations9 Jun 2023 Jiaxin Zhang, Bangdong Chen, Hiuyi Cheng, Fengjun Guo, Kai Ding, Lianwen Jin

Furthermore, considering the importance of fine-grained elements in document images, we present a details recurrent refinement module to enhance the output in a high-resolution space.

Self-Supervised Learning

Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild

1 code implementation23 Jul 2022 Jiaxin Zhang, Canjie Luo, Lianwen Jin, Fengjun Guo, Kai Ding

To address this issue, we propose a novel approach called Marior (Margin Removal and \Iterative Content Rectification).

Optical Character Recognition (OCR)

LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding

2 code implementations ACL 2022 Jiapeng Wang, Lianwen Jin, Kai Ding

LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models.

Document Image Classification document understanding +2

Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs

no code implementations13 Dec 2020 Michael Beyer, Andrey Morozov, Emil Valiev, Christoph Schorn, Lydia Gauerhof, Kai Ding, Klaus Janschek

The results demonstrate how random bit flips in the output of particular mathematical operations and layers of NNs affect the classification accuracy.

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