• 1
• 0
• 0

wanghuogen / chineseocr_lite

超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M

• 1
• 0
• 0

wanghuogen / scnn

Segment-CNN: A Framework for Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

• 1
• 3
• 1

wanghuogen / IdCardOCR

身份证正反面识别，身份证扫描识别，二代身份证OCR识别，OCR极速识别身份证所有信息正反面均可。离线无需联网,极速秒扫。

• 1
• 1
• 1

wanghuogen / time_series_forecasting_pytorch

time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code

• 1
• 1
• 1

wanghuogen / idcardocr-2

离线环境下第二代居民身份证信息识别

• 1
• 0
• 1

wanghuogen / java_baidu_ocr

(java_baidu_ocr)Java调用百度OCR文字识别API实现图片文字识别软件

• 1
• 0
• 0

wanghuogen / pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

• 2
• 0
• 0

wanghuogen / tsn-pytorch

• 1
• 0
• 0

wanghuogen / tensorflow-deeplab-resnet

DeepLab-ResNet rebuilt in TensorFlow

• 1
• 0
• 0

wanghuogen / ConvLSTM_pytorch

Implementation of Convolutional LSTM in PyTorch.

• 1
• 0
• 0

wanghuogen / Convolution_LSTM_PyTorch

Multi-layer convolutional LSTM with Pytorch

• 1
• 1
• 0

wanghuogen / idCardOcr-1

身份证识别

• 1
• 0
• 0

wanghuogen / fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

• 1
• 0
• 0

wanghuogen / Ubuntu16.04-Install-Opencv2.4.13

• 1
• 0
• 0

wanghuogen / CCF-OCR

身份证复印件识别，CCF BDCI参赛项目

• 1
• 0
• 0

wanghuogen / awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

• 1
• 0
• 0

wanghuogen / netron

Visualizer for neural network, deep learning and machine learning models

• 1
• 0
• 0

wanghuogen / opencv-2.4-cuda-9-patch

This is a quick patch for compiling OpenCV 2.4.x with CUDA 9.

• 1
• 0
• 0

wanghuogen / MSIfromHE

predict microsatellite status from hematoxylin-eosin histology of gastrointestinal cancer

• 1
• 0
• 0

wanghuogen / PlotNeuralNet

Latex code for making neural networks diagrams