The document discusses machine learning and optical character recognition (OCR). It defines machine learning as the study of algorithms that can learn from data without being explicitly programmed. It discusses the types of machine learning algorithms and applications such as spam detection and medical research. The aim of the presentation is to implement OCR using supervised machine learning to convert images of text into machine-encoded text. It describes the OCR process and prospects for using OCR in applications like processing business documents.

1. Machine Learning
(OCR)
Presented By-
Raunaq Kataria
Amit Gupta
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2. Overview
1. What is it ?
2. What are the Applications ?
3. What are the types of ML Algorithms ?
4. Why Machine Learning ?
5. What is our Aim ?
6. OCR
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3. What is Machine Learning ?
• Study of Algorithms that can ‘learn from data’
through iterations (experience)
• Without being ‘explicitly programmed’
• Operation:
1. Build models on Inputs.
2. Making Predictions
3. E.g. Predicting Flight Delays
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4. Applications
• Spam mail (recognition)
• Web click data (recommendations)
• Security (Pattern recognition, face detection)
• Business (Stocks, user behaviors)
• Medical (Research on medical records)
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5. Types Of Algorithms
• There are mainly 2 types of ML Algorithms:
1. Supervised
2. Unsupervised
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6. Why Machine Learning?
• Beginning towards the aim of Ultimate A.I.
i.e.
1. Self Aware
2. Fully Conscious
3. As Intelligent as The Human Race.
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7. Our Aim
• Implementing Machine Learning Algorithms to
execute :
OCR : Optical Character Recognition
using Supervised Machine Learning Algorithm
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8. What is OCR ?
• OCR :
1. First step in the field of Computer Vision
2. A part of AI
• Conversion of images of Handwritten or printed text into
machine encoded text
• Implement on MATLAB through supervised learning
algorithm
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9. INPUT
TYPEWRITTEN TEXT
IMAGE
OUTPUT
MACHINE
ENCODED TEXT
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10. Preprocessing
• converted
Pixel Intensities
(in Matrix Forms)
into two matrices
Large no of
training examples
(say: 5000)
X : rows = no. of training examples
columns = pixel intensities
Y : rows = no. of labels under which
the training examples are classified
Training Data
Set
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11. TRAINING
DATA SET
Output
LEARNING ALGO
(logistic regression)
Hypothesis
Function (h)
Input Data Set
<
Partial Output
‘n’ iterations

12. OCR - PROSPECTS
• Extract key info from business documents,
e.g. insurance policy, passport, bank statement etc.
• Automatic number plate recognition
• Assistive technology for visually impaired users
( text-to-speech converter)
• Extracting business card info into a contact list
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13. BIBLIOGRAPHY
• Lectures by - (Andrew Ng, Machine Learning-
Stanford University )
• http://en.wikipedia.org/wiki/Machine_learning
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14. Thank You
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