Codes, Source, Results and Report of Internship at the Basirtech company.

Project contains:

1. PCA
2. SVM
3. Tuning
4. Neural Network
5. Shape Context Descriptor
6. Report

I use sklearn digit dataset. My aim was to reduce dimention of these images to get at least 99% variance. Sklearn digit is 8*8 images. With only 39 digits I get 99% variance.

Fitting Svm with RBF kernel to the reduced dimention dataset by PCA and plotting recall and precission.

• Precission score for train dataset: 1
• Precission score for test dataset: 0.9902
• Recall score for train dataset: 1
• Recall score for test dataset: 0.9907

In this part I use Mnist digit dataset. I used ⅼibsvⅿ library to implement SVM. Then I evaluate my best classifier with evaluation dataset to accurate the precission. After I finalized my classifier, I predict test dataset.

• Accuracy for train dataset: 99.37%
• Accuracy for test dataset: 98.34%

I extract the images with wrong labeled by My final SVM. Most of them was low quality and hard to label by human being. I also create a text file containing name of wrong labeled images and the probability of labeling in these images. I also plot a histogram of these probabilities for train and test dataset.

Designing two Neural Network model with tensorfⅼow.

• One layer fully connected
• One layer localy connected + one layer fully connected

For each model I first train my model and then find its precision and loss.

• Model 1 had 0.933 precision and 0.24 loss.
• Model 2 had 0.9984 precision and 0.013 loss.

I tried to create a log-polar histogram for each image and then I use this histogram as feature vector. For each image, I found the bigest contour on each image and set the points lying on the the contour as my points needed for Shape Context Descriptor. Then I tune a SVM model on the feature vector created from log-polar histogram and I found accuracy for train and test dataset.

• Accuracy for train dataset: 92.19%
• Accuracy for test dataset: 66.68%