character recognition using hidden markov models anthony dipirro ji mei sponsor:prof. william...
TRANSCRIPT
![Page 1: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/1.jpg)
Character Recognition using Hidden Markov Models
Anthony DiPirroJi Mei
Sponsor:Prof. William Sverdlik
![Page 2: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/2.jpg)
Our goal
Recognize handwritten Roman and Chinese characters
This is an example of the Noisy Channel Problem
Ji
![Page 3: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/3.jpg)
Noisy Channel Problem• Find the intended input, given the noisy input
that was received
• Examples
– iPhone 4S Siri speech recognition
– Human handwriting
![Page 4: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/4.jpg)
Markov Chain
We use a Hidden Markov Model to solve the Noisy Channel Problem
A HMM is a Markov chain for which the state is only partially observable.
Markov Chain Definition
Illustration
![Page 5: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/5.jpg)
Hidden Markov Model
![Page 6: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/6.jpg)
Our Project
![Page 7: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/7.jpg)
![Page 8: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/8.jpg)
How to solve our problem?
• Using a HMM, we can calculate the hidden states chain, based on the observation chain
• We used our collected samples to calculate transition probability table and emission probability table
• Use Viterbi algorithm to find the most likely result
![Page 9: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/9.jpg)
Pre-Processing
• Shrink
• Medium filter
• Sharpen
![Page 10: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/10.jpg)
Feature Extraction
• We count the regions in each area to represent the observation states
![Page 11: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/11.jpg)
Compare
Compare
Adjusted Input
Canonical B
Canonical A
…
S2S2
S2 S2
S3
S3 S3
S1
S2S2
S3 S3
![Page 12: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/12.jpg)
ExperimentingHow to split character
![Page 13: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/13.jpg)
ExperimentingHow to represent states
![Page 14: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/14.jpg)
Result
![Page 15: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/15.jpg)
Conclusions
• Factors that will affect accuracy
– Pre-processing
–How to split word
–Number of states
![Page 16: Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik](https://reader034.vdocuments.site/reader034/viewer/2022042615/56649c7d5503460f94931c45/html5/thumbnails/16.jpg)
In the future
• Spend more time on different features
Pixel Density
Counting lines
• Use other algorithms such as a neural network to implement character recognition.