computer vision, image processing and machine learning on the web browser or node.

Last update: Nov 11, 2022

Overview

Image processing and Machine learning labs

computer vision, image processing and machine learning on the web browser or node

note

Fast Fourier Transform (1D/2D-FFT)
Stereo Matching
Poisson Image Editing
Line Segment Detector
Corner Detection
Fish-Eye Transform
Image Processing Filters
Image Histogram Calculation
Image Feature Extraction
Decision Tree Learning
K-Means++ Clustering
Logistic Regression
Adaptive Regularization of Weight Vectors (AROW)
Soft Confidence Weighted Learning (SCW)
Gradient Boosting Decision Tree (GBDT)
Neural Network (Denoising Autoencoders)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
3D Shape Drawing (Mobius Strip, Klein Bottle, Heart Surface ...)
WebGL Samples
etc..

demo

Demo Site (mirror)

license

Owner

ryohei tanaka

software engineer

GitHub Repository

Augmenting Anchors by the Detector Itself

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Sort By Face

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Scene text recognition

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Ocular is a state-of-the-art historical OCR system.

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Rubik's Cube in pygame with OpenGL

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Convert Text-to Handwriting Using Python

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The code for CVPR2022 paper "Likert Scoring with Grade Decoupling for Long-term Action Assessment".

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Image processing in Python

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Provides OCR (Optical Character Recognition) services through web applications

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第一届西安交通大学人工智能实践大赛（2018AI实践大赛--图片文字识别）第一名；仅采用densenet识别图中文字

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441 Dec 22, 2022

A pure pytorch implemented ocr project including text detection and recognition

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This is a real life mario project using python and mediapipe

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Write-ups for the SwissHackingChallenge2021 CTF.

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OCR-D-compliant page segmentation

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Code for CVPR'2022 paper ✨ "Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model"

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CUTIE (TensorFlow implementation of Convolutional Universal Text Information Extractor)

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Reference Code for AAAI-20 paper "Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels"

Reference Code for AAAI-20 paper "Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels" Please refer to htt

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