|
--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': A |
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'1': B |
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'2': C |
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'3': D |
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'4': E |
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'5': F |
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'6': G |
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'7': H |
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'8': I |
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'9': J |
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'10': K |
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'11': L |
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'12': M |
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'13': 'N' |
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'14': O |
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'15': P |
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'16': Q |
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'17': R |
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'18': S |
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'19': T |
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'20': U |
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'21': V |
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'22': W |
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'23': X |
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'24': 'Y' |
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'25': Z |
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splits: |
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- name: train |
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num_bytes: 22453522 |
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num_examples: 26000 |
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- name: test |
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num_bytes: 2244964.8 |
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num_examples: 2600 |
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download_size: 8149945 |
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dataset_size: 24698486.8 |
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task_categories: |
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- image-classification |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for "letter_recognition" |
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|
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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). |
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|
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|
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```python |
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import os |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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|
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CHARACTER_COUNT = 26 |
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|
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data = pd.read_csv('./A_Z Handwritten Data.csv') |
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mapping = {str(i): chr(i+65) for i in range(26)} |
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|
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def generate_dataset(folder, end, start=0): |
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if not os.path.exists(folder): |
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os.makedirs(folder) |
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print(f"The folder '{folder}' has been created successfully!") |
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else: |
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print(f"The folder '{folder}' already exists.") |
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|
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for i in range(CHARACTER_COUNT): |
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dd = data[data['0']==i] |
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for j in range(start, end): |
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ddd = dd.iloc[j] |
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x = ddd[1:].values |
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x = x.reshape((28, 28)) |
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plt.axis('off') |
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plt.imsave(f'{folder}/{mapping[str(i)]}_{j}.jpg', x, cmap='binary') |
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|
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generate_dataset('./train', 1000) |
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generate_dataset('./test', 1100, 1000) |
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``` |
