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ocr

Optical character recognition using neural network. Implemented with Python and its libraries Numpy and OpenCV.

ocr-1

Optical Character Recognition using openCV, Python

ocr-2

Optical Character Recognition system for math formulas

ocr-cnn-knn-svm-random-forest

ocr-convert-image-to-text

:clipboard: Python wrapper to grab text from all the images in a directory and save a subdirectory as text files using Tesseract Engine

ocr-crnn-ctc

ocr, cnn+lstm+ctc, crnn, recognition model, tensorflow

ocropy

Python-based tools for document analysis and OCR

ofxmsatensorflow

openframeworks addon + examples for Google's TensorFlow numerical computation and machine learning library

ogmadrive

Ogma - OgmaDrive https://ogma.ai/

ogmaneo

Ogma - OgmaNeo

oh-distro

An integrated humanoid control, planning and perception system. Developed by MIT and the University of Edinburgh for the Boston Dynamics Atlas and the NASA Valkyrie humanoid robots

omg-adsc

Source code for ADSC team's submissions to OMG Emotion Challenge 2018

omgemotionchallenge

Repository for th OMG Emotion Challenge

one-pixel-attack-keras

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10

open-tracking-tools

Open source Java library for on-line vehicle tracking, path estimation and parameter learning.

openalpr

Automatic License Plate Recognition library

openauto

AndroidAuto headunit emulator

openbci_gui

OpenBCI_GUI is the cross platform application for the OpenBCI Cyton and Ganglion. Tested on Mac, Windows and Ubuntu/Mint Linux. Ganglion requires some extra configuration on Linux/Windows.

opencv

Open Source Computer Vision Library

opencv-1

Based on the udemy course "Introduction to Computer Vision | Master OpenCV in Python" by Rajeev Ratan

opencv-3.2.0-compiling-on-raspberry-pi

Download, Compile, Build, and Install OpenCV 3.2.0 with Extra Modules on RPI running Jessie

opencv-car-number-plate-detection-

this project is made using python and opencv . i haven't used anything machine learning or rendering stuff to detect number plate . It is based on contour in opencv . it's dosent give you numbers directly it's just highlights (gives you the coordinate ) the rectangular area of number plate in image

opencv-face-recognition

Real-time face recognition project with OpenCV and Python

opencv-face-recognition-python

Face Recognition using OpenCV and Python.

opencv-python-blueprints

OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python

opencv3_tutorials

These are the codes I used in my youtube videos

opencv_3_license_plate_recognition_python

opencv_hummingbirdsmotion

Motion Detection using OpenCV and python

opencv_project1

Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. For this, haar features shown in below image are used. They are just like our convolutional kernel. Each feature is a single value obtained by subtracting sum of pixels under white rectangle from sum of pixels under black rectangle. haar_features.jpg image Now all possible sizes and locations of each kernel is used to calculate plenty of features. (Just imagine how much computation it needs? Even a 24x24 window results over 160000 features). For each feature calculation, we need to find sum of pixels under white and black rectangles. To solve this, they introduced the integral images. It simplifies calculation of sum of pixels, how large may be the number of pixels, to an operation involving just four pixels. Nice, isn't it? It makes things super-fast. But among all these features we calculated, most of them are irrelevant. For example, consider the image below. Top row shows two good features. The first feature selected seems to focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. The second feature selected relies on the property that the eyes are darker than the bridge of the nose. But the same windows applying on cheeks or any other place is irrelevant. So how do we select the best features out of 160000+ features? It is achieved by Adaboost. haar.png image For this, we apply each and every feature on all the training images. For each feature, it finds the best threshold which will classify the faces to positive and negative. But obviously, there will be errors or misclassifications. We select the features with minimum error rate, which means they are the features that best classifies the face and non-face images. (The process is not as simple as this. Each image is given an equal weight in the beginning. After each classification, weights of misclassified images are increased. Then again same process is done. New error rates are calculated. Also new weights. The process is continued until required accuracy or error rate is achieved or required number of features are found). Final classifier is a weighted sum of these weak classifiers. It is called weak because it alone can't classify the image, but together with others forms a strong classifier. The paper says even 200 features provide detection with 95% accuracy. Their final setup had around 6000 features. (Imagine a reduction from 160000+ features to 6000 features. That is a big gain). So now you take an image. Take each 24x24 window. Apply 6000 features to it. Check if it is face or not. Wow.. Wow.. Isn't it a little inefficient and time consuming? Yes, it is. Authors have a good solution for that. In an image, most of the image region is non-face region. So it is a better idea to have a simple method to check if a window is not a face region. If it is not, discard it in a single shot. Don't process it again. Instead focus on region where there can be a face. This way, we can find more time to check a possible face region. For this they introduced the concept of Cascade of Classifiers. Instead of applying all the 6000 features on a window, group the features into different stages of classifiers and apply one-by-one. (Normally first few stages will contain very less number of features). If a window fails the first stage, discard it. We don't consider remaining features on it. If it passes, apply the second stage of features and continue the process. The window which passes all stages is a face region. How is the plan !!! Authors' detector had 6000+ features with 38 stages with 1, 10, 25, 25 and 50 features in first five stages. (Two features in the above image is actually obtained as the best two features from Adaboost). According to authors, on an average, 10 features out of 6000+ are evaluated per sub-window. So this is a simple intuitive explanation of how Viola-Jones face detection works. Read paper for more details or check out the references in Additional Resources section. Haar-cascade Detection in OpenCV OpenCV comes with a trainer as well as detector. If you want to train your own classifier for any object like car, planes etc. you can use OpenCV to create one. Its full details are given here: Cascade Classifier Training. Here we will deal with detection. OpenCV already contains many pre-trained classifiers for face, eyes, smile etc. Those XML files are stored in opencv/data/haarcascades/ folder. Let's create face and eye detector with OpenCV.

opencvandroid

This is sample code for object detection using OpenCV on android