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2019-CCF-BDCI-OCR-MCZJ-fake_data_generator
2019CCF-BDCI大赛 OCR赛题第一名 天晨破晓团队 仿真数据生成方案源码
2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement
2019CCF-BDCI大赛 最佳创新探索奖获得者 基于OCR身份证要素提取赛题冠军 天晨破晓团队 赛题源码
AdslProxy
ADSLProxyPool
Adsl Proxy Pool
AiLearning
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
bilibiliFans
lyrics-spider
爬虫采用的python的asyncio库实现了简单的异步爬取, 输入某个歌手的名字就可以下载此歌手所有专辑的音乐
TimePeriod-NLP
Multilingual-Multimodal-NLP
Challenging6's Repositories
Challenging6/TimePeriod-NLP
Challenging6/2019-CCF-BDCI-OCR-MCZJ-fake_data_generator
2019CCF-BDCI大赛 OCR赛题第一名 天晨破晓团队 仿真数据生成方案源码
Challenging6/2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement
2019CCF-BDCI大赛 最佳创新探索奖获得者 基于OCR身份证要素提取赛题冠军 天晨破晓团队 赛题源码
Challenging6/AttentionOCR
Scene text recognition
Challenging6/bisheng
Bisheng is an open LLM devops platform for next generation AI applications.
Challenging6/Box_Discretization_Network
Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019)
Challenging6/Challenging6
Challenging6/ChineseAddress_OCR
Photographing Chinese-Address OCR implemented using CTPN+CTC+Address Correction. 拍照文档中文地址文字识别。
Challenging6/DB
A PyToch implementation of "Real-time Scene Text Detection with Differentiable Binarization".
Challenging6/DBNet.pytorch
A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization
Challenging6/deep-text-recognition-benchmark
Text recognition (optical character recognition) with deep learning methods.
Challenging6/DocProj
Document Rectification and Illumination Correction using a Patch-based CNN
Challenging6/duorat
Challenging6/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.
Challenging6/imgaug
Image augmentation for machine learning experiments.
Challenging6/MaskTextSpotter
A PyTorch implementation of Mask TextSpotter
Challenging6/MegReader
A research project for text detection and recognition using PyTorch 1.2.
Challenging6/OCR-Corrector
利用语言模型，纠正OCR识别错误
Challenging6/OTR
Optical table recognition - recognize tables in scan images using OpenCV
Challenging6/PaddleOCR
Awesome multilingual OCR toolkits based on PaddlePaddle （practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices）
Challenging6/PMTD
Pyramid Mask Text Detector designed by SenseTime Video Intelligence Research team.
Challenging6/Prompt
Challenging6/sparc
scripts and baselines for SParC: Yale & Salesforce Semantic Parsing and Text-to-SQL in Context Challenge
Challenging6/splncs04nat
natbib compatible splncs04.bst (Springer LNCS) BibTeX Style File built using a docstrip with the conventional merlin.mbs master file.
Challenging6/stopwords
中文常用停用词表（哈工大停用词表、百度停用词表等）
Challenging6/TabularSemanticParsing
Translating natural language questions to a structured query language
Challenging6/TextGenerator
OCR dataset Text-Detection dataset Font-Classification dataset generator
Challenging6/TIES-2.0
Code for: S.R. Qasim, H. Mahmood, and F. Shafait, Rethinking Table Recognition using Graph Neural Networks (2019)
Challenging6/TIES_DataGeneration
Dataset Generation Code for: S.R. Qasim, H. Mahmood, and F. Shafait, Rethinking Table Parsing using Graph Neural Networks (2019)
Challenging6/YoloDB