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1. Batch Members :
T.Manivannan 513320104022
A.Rohinth 513320104038
P.Tamilmani 513320104703
Project Guide :
Mr.G.Dhanasekar M.E.,
CSE Department,
UCEA .
MORSE CODE DETECTION FROM
EYE BLINKS USING MACHINE
LEARNING

2. INTRODUCTION
• There are several medical conditions which leads to motor speech disorders or paralysis
such as Locked In Syndrome,Cerebral Palsy and Amyotrophic Lateral Sclerosis that
inhibits speech or voice production. This might cause the patient to lose their ability to
communicate with the people around them.
• In such cases people are aware of what they would like to say but are unable to
articulate their thoughts.
• The Morse code was developed in the early 19th century.The Morse code was named
so after its inventor, Samuel F B Morse.

3. ABSTRACT
• Communication with the outside world or the caretakers is one of the major
challenges for the people with disabilities.
• So,we propose a realtime algorithm to detect morse code from the eye blinks in a
series from a live camera.
• The low-cost software decodes morse code precisely based on the duration of the
eyes closed or opened which in turn is translated to English Language.
• So, this system is intended to provide an alternative form of communication for
people with disabilities and to convey confidential messages.

4. LITERATURE SURVEY
S.NO Author Name /
Tittle
Publication /
Year
Advantages Disadvantages /
Limitations
1
Liu, J., Li, X., Yang, M.,
& Peng
Journal of Physics
2019.
Natural eye blinking
actions makes
accessible to a wide
range of individuals,
including those with
physical disabilities.
Involuntary or unintentional
blinks, leading to potential
errors in Morse code
interpretation.
2 Sandeep Varma N IEEE Mysore Sub
Section International
Conference
2021.
More reliable, secure,
user-friendly and a cost-
effective.
Sometimes face detection is
difficult.
3
P.Sai Deepak
V.Somanath
Padmaveni Krishnan
E.Monish Ram Reddy
2021 5th International
conference on
information system and
computer networks
Helps an avarage
visually challenged
person to detect nearby
object.
1.Cost effective
2.unportability

5. EXISTING SYSTEM
• Head motion detection : Sensors or cameras to detect head movements, such as
nods, shakes, or tilts, which will serve as inputs for Morse code signals.
• Morse code detecting: To detect the head movements as Morse code characters.
Each distinct head motion pattern would correspond to a specific Morse code
symbol (e.g., short movement for dots, long movement for dashes).
• Wi-Fi transmission: Establish a Wi-Fi connection between the transmitting
device and the receiving device.

6. DISADVANTAGES
• Reliability: Head motions may not always be accurately detected, leading to
errors in Morse code transmission. This could result in miscommunication or
incomplete messages.
• Limited Range: The range of Wi-Fi signals may be limited, restricting the distance
over which Morse code communication can take place.
• Security Concerns: Transmitting Morse code signals over Wi-Fi could raise
security concerns, as it may be susceptible to interception or hacking if proper
encryption measures are not implemented.

7. PROPOSED SYSTEM
• Our project is based on detection of morse code from eye blinks. As there are
many ways to detect a morse code like by using mouth, fingers or by using
hands. We will be detecting morse code from a pattern of eye blink.
• Before detecting morse code from eye blinks, we first have to detect eyes.
Morse code detection has 3 major steps:
1) Locating eyes
2) determining Eye Aspect Ratio
3) detecting morse code from blinks
• Morse code is generated from the pattern of blinks that is when the user
closes his eyes for 1 second a ’.’(dot) is detected and when the user closes his
eyes for 2 seconds a ’-’(dash) is detected. This dot and dash are stored and
then converted into English Language.

8. ADVANTAGES
• Accuracy: With advancements in eye-tracking technology, the accuracy of
detecting eye blinks for Morse code communication can be high, minimizing
errors in message transmission.
• Privacy: Morse code communication via eye blinks can be discreet, offering
privacy in public settings or situations where verbal communication may not be
desirable.
• Low Power Consumption: Eye blink detection systems can be designed to be
energy-efficient, prolonging battery life in portable devices.

9. REFERENCE
• Kühnlenz, K., Danelakis, A., Tseranidou, A., & Stork, W. (2017).
Eyeblink-based, non-invasive communication. In 2017 International
Conference of the IEEE.
• Sivan, Y., & Bentin, S. (2019). Eye blinks in spontaneous and
intentionally suppressed facial expressions. Psychological Science.