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「一个」、「Time 时光」、「开眼」、「一席」、「梨视频」、「微软必应词典」、「金山词典」、「豆瓣电影」、「**天气」、「魅族天气」、「每日一文」、「12306」、「途牛」、「快递100」、「快递」应用 Api。仅供学习，禁止商业使用，侵权请联系删除。

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

attentionocr

Scene text recognition

bdci2018-chinauuicom-1st-solution

这是BDCI2018的联通赛题第一名解决方案

bilibilifans

bisheng

Bisheng is an open LLM devops platform for next generation AI applications.

box_discretization_network

Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019)

buaa-2019-computer-lecture-homework

2019北航微软计算机前沿讲座挑战作业

buaa_mips_os

北航小操作系统实验代码lab1~lab6

bugcatcher

捉虫记

cet46-queryscores

challenging6

challenging6.github.io

chineseaddress_ocr

Photographing Chinese-Address OCR implemented using CTPN+CTC+Address Correction. 拍照文档中文地址文字识别。

db

A PyToch implementation of "Real-time Scene Text Detection with Differentiable Binarization".

dbnet.pytorch

A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization

deep-text-recognition-benchmark

Text recognition (optical character recognition) with deep learning methods.

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为17个章节，20多万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

docproj

Document Rectification and Illumination Correction using a Patch-based CNN

duorat

evacuation_data

gerapy

Distributed Management Framework Based on Scrapy and Scrapyd

houseinfospiderdistributed

基于Scrapy-Redis框架的分布式爬虫

icdar-sroie

ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction

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.

imgaug

Image augmentation for machine learning experiments.