Datasets:
File size: 2,037 Bytes
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---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
'4': E
'5': F
'6': G
'7': H
'8': I
'9': J
'10': K
'11': L
'12': M
'13': 'N'
'14': O
'15': P
'16': Q
'17': R
'18': S
'19': T
'20': U
'21': V
'22': W
'23': X
'24': 'Y'
'25': Z
splits:
- name: train
num_bytes: 22453522
num_examples: 26000
- name: test
num_bytes: 2244964.8
num_examples: 2600
download_size: 8149945
dataset_size: 24698486.8
task_categories:
- image-classification
language:
- en
size_categories:
- 1K<n<10K
---
# 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](https://www.kaggle.com/datasets/sachinpatel21/az-handwritten-alphabets-in-csv-format).
```python
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)
``` |