pittawat/letter_recognition
Dataset Card for "letter_recognition" Images in this dataset was generated using the script defined below. The original dataset in CSV format and more information of the original dataset is available at A-Z Handwritten Alphabets in .csv format. import os import pandas as pd import matplotlib.pyplot as plt CHARACTER_COUNT = 26 data = pd.read_csv('./A_Z Handwritten Data.csv') mapping = {str(i): chr(i+65) for i in range(26)} def generate_dataset(folder, end, start=0): if not… See the full description on the dataset page: https://huggingface.co/datasets/pittawat/letter_recognition.
Dataset Card for "letter_recognition"
Images in this dataset was generated using the script defined below. The original dataset in CSV format and more information of the original dataset is available at A-Z Handwritten Alphabets in .csv format.
import os
import pandas as pd
import matplotlib.pyplot as plt
CHARACTER_COUNT = 26
data = pd.read_csv('./A_Z Handwritten Data.csv')
mapping = {str(i): chr(i+65) for i in range(26)}
def generate_dataset(folder, end, start=0):
if not os.path.exists(folder):
os.makedirs(folder)
print(f"The folder '{folder}' has been created successfully!")
else:
print(f"The folder '{folder}' already exists.")
for i in range(CHARACTER_COUNT):
dd = data[data['0']==i]
for j in range(start, end):
ddd = dd.iloc[j]
x = ddd[1:].values
x = x.reshape((28, 28))
plt.axis('off')
plt.imsave(f'{folder}/{mapping[str(i)]}_{j}.jpg', x, cmap='binary')
generate_dataset('./train', 1000)
generate_dataset('./test', 1100, 1000)