Pinned Repositories
attend-copy-parse
Code for paper Attend, Copy, Parse - End-to-end information extraction from documents (https://arxiv.org/abs/1812.07248)
CRAFT-Reimplementation
CRAFT-Pyotorch：Character Region Awareness for Text Detection Reimplementation for Pytorch
deep_learning_cookbook
Deep Learning Cookbox
Depix
Recovers passwords from pixelized screenshots
ICDAR2019_cTDaR
The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.
learning-spark
Example code from Learning Spark book
Leetcode-Solution-Notes
Record of my programming experience on leetcode.Learning from dalaos...
MISC
OnJava8
《On Java 8》中文版，又名《Java编程**》 第5版
PMTD
Pyramid Mask Text Detector designed by SenseTime Video Intelligence Research team.
EmperorKaiser's Repositories
EmperorKaiser/attend-copy-parse
Code for paper Attend, Copy, Parse - End-to-end information extraction from documents (https://arxiv.org/abs/1812.07248)
EmperorKaiser/CRAFT-Reimplementation
CRAFT-Pyotorch：Character Region Awareness for Text Detection Reimplementation for Pytorch
EmperorKaiser/deep_learning_cookbook
Deep Learning Cookbox
EmperorKaiser/Depix
Recovers passwords from pixelized screenshots
EmperorKaiser/ICDAR2019_cTDaR
The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.
EmperorKaiser/learning-spark
Example code from Learning Spark book
EmperorKaiser/Leetcode-Solution-Notes
Record of my programming experience on leetcode.Learning from dalaos...
EmperorKaiser/MISC
EmperorKaiser/OnJava8
《On Java 8》中文版，又名《Java编程**》 第5版
EmperorKaiser/PMTD
Pyramid Mask Text Detector designed by SenseTime Video Intelligence Research team.
EmperorKaiser/PRML
PRML algorithms implemented in Python
EmperorKaiser/Python-100-Days
Python - 100天从新手到大师
EmperorKaiser/python_practice_of_data_analysis_and_mining
《Python数据分析与挖掘实战》随书源码与数据
EmperorKaiser/ReS2TIM
ReS2TIM: Reconstruct Syntactic Structures from Table Images
EmperorKaiser/research_tao
NLP研究入门之道
EmperorKaiser/self-supervsed_edge_feats
code for "Self-supervised edge features for improved Graph Neural Network training", <arxivlink>
EmperorKaiser/TableMASTER-mmocr
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.
EmperorKaiser/TableQA
NL2SQL competition dataset
EmperorKaiser/TabularCellTypeClassification
Code and experiment data for ICDM'19 paper, tabular cell classification using pre-trained cell embeddings. Note that the code and data is cleaned for release and is not the exact version used in the paper, and may not produce exact results.
EmperorKaiser/TensorFlow_Exercises
The codes I made while I practiced various TensorFlow examples
EmperorKaiser/TGRNet
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
EmperorKaiser/TUTA_table_understanding