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6.30_lesson1

ai_challenger_2018

AI Challenger, a platform for open datasets and programming competitions to artificial intelligence (AI) talents around the world. https://challenger.ai/

assignment-lesson-of-kaikeba

assignment3-lesson3

attention-ocr

Visual Attention based OCR

attention_ocr.pytorch

This repository implements the the encoder and decoder model with attention model for OCR

awesome-synthtext

A curated list of awesome synthetic data for text location and recognition

chinese-ocr

[python3.6] 运用tf实现自然场景文字检测,keras/pytorch实现ctpn+crnn+ctc实现不定长场景文字OCR识别

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

cnocr

A python package for Chinese OCR with the available pre-trained model. So it can be used directly after installed.

corner

Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation

craft-pytorch

Official implementation of Character Region Awareness for Text Detection (CRAFT)

craft_pytorch

A reimplementation of Character Region Awareness for Text Detection (CRAFT) with training code

crnn_tensorflow

Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition

darknet

Windows and Linux version of Darknet Yolo v4 (v3/v2) Neural Networks for object detection (Tensor Cores are used)

hog_svm

使用HOG+SVM进行图像分类

holy-edge

Holistically-Nested Edge Detection

invoice

增值税发票OCR识别，使用flask微服务架构，识别type：增值税电子普通发票，增值税普通发票，增值税专用发票；识别字段为：发票代码、发票号码、开票日期、校验码、税后金额等

jupyters_and_slides

keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

lab

[CVPR 2018] Look at Boundary: A Boundary-Aware Face Alignment Algorithm

lets_ocr

A repository for OCR, which inlcudes some classical OCR algorithms Pytorch implementation such as CTPN, EAST and CRNN.

models

Models and examples built with TensorFlow

my_orc_keras_verification_code_identification

本项目实现了ocr主流算法gru/lstm+ctc+cnn架构，进行不定长度验证码识别，达到不分割字符而识别验证码内容的效果。验证码内容包含了大小字母以及数字，并增加点、线、颜色、位置、字体等干扰项。本项目对gru +ctc+cnn、lstm+ctc+cnn、cnn三种架构进行了对比，实践说明同等训练下gru/lstm+ctc+cnn架构准确率和速度均明显优于cnn架构，gru +ctc+cnn优于lstm+ctc+cnn，在实验2500个样本数据200轮训练时，gru +ctc+cnn架构在500样本测试准确率达90.2%。本项目技术能够训练长序列的ocr识别，更换数据集和相关调整，即可用于比如身份证号码、车牌、手机号、邮编等识别任务，也可用于汉字识别。

pixel_link

Implementation of our paper 'PixelLink: Detecting Scene Text via Instance Segmentation' in AAAI2018

project-1

项目1

pythoncomputervision-9-image-content-classification

图像内容分类。本文主要阐述：①knn可视化。②dense sift（稠密sift）原理。③手势识别

pytorch-unet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

r2cnn_fpn_tensorflow

R2CNN: Rotational Region CNN Based on FPN (Tensorflow)

spider_captcha

图像识别学习！