https://github.com/kmohamedalie/letter_recognition

Alphabet recognition
https://github.com/kmohamedalie/letter_recognition

boosting-algorithms computer-science gpu ibm letter-recognition machine-learning random-forest snapml uci-machine-learning

Last synced: 6 days ago
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Alphabet recognition

• Host: GitHub
• URL: https://github.com/kmohamedalie/letter_recognition
• Owner: Kmohamedalie
• License: mit
• Created: 2023-08-14T15:31:04.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-08-20T10:46:39.000Z (about 1 year ago)
• Last Synced: 2023-08-20T11:53:06.105Z (about 1 year ago)
• Topics: boosting-algorithms, computer-science, gpu, ibm, letter-recognition, machine-learning, random-forest, snapml, uci-machine-learning
• Language: Jupyter Notebook
• Homepage: https://github.com/Kmohamedalie/Letter_Recognition/tree/master
• Size: 1.07 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Letter  Recognition using IBM SnapML Algorithms

![image](https://github.com/Kmohamedalie/Letter_Recognition/assets/63104472/040c2ba2-fe1b-4bea-888b-2b0b4f0f01aa)

**Task:** Database of character image features; try to identify the letter

**Dataset:** Kaggle, UCI Machine Learning

**Complete JupyterNotebook:** [Link](https://github.com/Kmohamedalie/Letter_Recognition/tree/master/Notebook)

**Metrics:**

| Algorithm | Precision | Recall | F1-score | Accuracy |

|-----------|-----------|--------|----------|----------|

| Random Forest   | 93.87%    | 93.87%  | 93.87% | 93.87%      |

## Additional Information about the dataset

The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet.  The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli.  Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15.  We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000.  See the article cited above for more details.

## Attribute

	 1.	lettr	capital letter	(26 values from A to Z)

	 2.	x-box	horizontal position of box	(integer)

	 3.	y-box	vertical position of box	(integer)

	 4.	width	width of box			(integer)

	 5.	high 	height of box			(integer)

	 6.	onpix	total # on pixels		(integer)

	 7.	x-bar	mean x of on pixels in box	(integer)

	 8.	y-bar	mean y of on pixels in box	(integer)

	 9.	x2bar	mean x variance			(integer)

	10.	y2bar	mean y variance			(integer)

	11.	xybar	mean x y correlation		(integer)

	12.	x2ybr	mean of x * x * y		(integer)

	13.	xy2br	mean of x * y * y		(integer)

	14.	x-ege	mean edge count left to right	(integer)

	15.	xegvy	correlation of x-ege with y	(integer)

	16.	y-ege	mean edge count bottom to top	(integer)

	17.	yegvx	correlation of y-ege with x	(integer)