Normalization (sociology)

Computer science

Anthropology

Natural language processing

Optical character recognition

Database

MNIST database

Character recognition

Pattern recognition (psychology)

Image (mathematics)

Intelligent character recognition

Sociology

Artificial intelligence

Deep learning

Intelligent word recognition

Artificial neural network

Machine learning

Speech recognition

Digit recognition

IEEE Signal Processing Magazine

In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research. Handwritten digit recognition is an important problem in optical character recognition, and it has been used as a test case for theories of pattern recognition and machine learning algorithms for many years. Historically, to promote machine learning and pattern recognition research, several standard databases have emerged in which the handwritten digits are preprocessed, including segmentation and normalization, so that researchers can compare recognition results of their techniques on a common basis. The freely available MNIST database of handwritten digits has become a standard for fast-testing machine learning algorithms for this purpose. The simplicity of this task is analogous to the TIDigit (a speech database created by Texas Instruments) task in speech recognition. Just like there is a long list for more complex speech recognition tasks, there are many more difficult and challenging tasks for image recognition and computer vision, which will not be addressed in this column.

The MNIST Database of Handwritten Digit Images for Machine Learning Research