Recognition of MNIST handwritten digits and character set research

Optical Character Recognition: Classification of Handwritten Digits and Computer Fonts

Optical character Recognition (OCR) is an important application of machine learning where an algorithm is trained on a data set of known letters/digits and can learn to accurately classify letters/digits. A variety of algorithms have shown excellent accuracy for the problem of handwritten digits, 4 of which are looked at here. Additionally, we attempt to extend these techniques to the harder pr...

Training Set Expansion in Handwritten Character Recognition

In this paper, a process of expansion of the training set by synthetic generation of handwritten uppercase letters via deformations of natural images is tested in combination with an approximate k−Nearest Neighbor (k−NN) classifier. It has been previously shown [11] [10] that approximate nearest neighbors search in large databases can be successfully used in an OCR task, and that significant pe...

Handwritten Digits Recognition

Handwritten digits recognition using OpenCV

The automated recognition of handwritten digits is a largely studied problem which connects the fields of Computer Vision and Machine Learning and has many applications in real life. In this project, I detail an introductory investigation of the performance of classification in several contexts. Namely, relying on the OpenCV implementations of k-Nearest Neighbor, Random Forests, and Support Vec...

Improved method of handwritten digit recognition tested on MNIST database

We have developed a novel neural classifier LImited Receptive Area (LIRA) for the image recognition. The classifier LIRA contains three neuron layers: sensor, associative and output layers. The sensor layer is connected with the associative layer with no modifiable random connections and the associative layer is connected with the output layer with trainable connections. The training process co...