(based on tensorflow-gpu 2.1.0)
LeNet5 Structure
CNN Model Structure
activators: tanh
EMNIST Letters: 145,600 characters. 26 balanced classes.
from US National Institute of Standards and Technology
The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset. Further information on the dataset contents and conversion process can be found in the paper available at https://arxiv.org/abs/1702.05373v1
The dataset is provided in two file formats. Both versions of the dataset contain identical information, and are provided entirely for the sake of convenience. The first dataset is provided in a Matlab format that is accessible through both Matlab and Python (using the scipy.io.loadmat function). The second version of the dataset is provided in the same binary format as the original MNIST dataset as outlined in http://yann.lecun.com/exdb/mnist/
- EMNIST ByClass: 814,255 characters. 62 unbalanced classes.
- EMNIST ByMerge: 814,255 characters. 47 unbalanced classes.
- EMNIST Balanced: 131,600 characters. 47 balanced classes.
- EMNIST Letters: 145,600 characters. 26 balanced classes.
- EMNIST Digits: 280,000 characters. 10 balanced classes.
- EMNIST MNIST: 70,000 characters. 10 balanced classes.
...
- Readme.txt file
- Binary format as the original MNIST dataset
- Matlab format dataset
- EMNIST paper, available at: https://arxiv.org/abs/1702.05373v1
model training
model structure output
training
recognizing correct
example 1 bold A
photograph
read in python and transform into 28*28
three channels into one channel
recognize result
correct!
example 2 normal q
photograph
read in python and transform into 28*28
three channels into one channel
recognize result
correct!
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