Real time-handwritten-devanagari-character-recoginition

1. REAL TIME HANDWRITTEN
DEVANAGARI CHARACTER
RECOGNITION

2.  Introduction
 Objectives
 Scope and application
 Features
 System diagrams
 Methodology
 Technologies used
 Future recommendation
 Results
Overview

3.  A Real time Devanagari handwritten character recognition
 Converts to digital text
 Recognize only Devanagari character
 Developed for android mobiles
 Based on pattern matching using signatures
Introduction

4.  To build the system that recognize the Real time
handwritten Devanagari character
 To build user friendly input gesture for providing any style
of Devanagari character
Objectives

5.  Improve of human computer interface for computer illiterate
people by providing various computing services on inputs
 Can be implemented on smart phones, tablets as an virtual
keyboard
 The system can create paperless environment by digitizing
handwritten character
Scope & Applications

6.  User can write the Devanagari character on their own styles
 Output will be displayed as a digital text
 User friendly GUI
 Attractive input gesture
Features

7. System Flow Diagram

8. Use Case Diagram

9. Training
 Pre-processing
 Feature Extraction
 Trained pattern
Recoginition
 Pre-processing
 Feature extraction
 Character Recognition
 Post processing
Methodology

10.  The process of smoothing the data by removing the noise is
pre-processing
 Gaussian low pass filter is used to remove noise and smooth
the data
 The equation for the Gaussian low pass filter is
where, σ is the standard deviation of the distribution
Pre-processing

11.  Extracting the features from the input samples
 Features are number of strokes, length of stroke, width-
height ratio, number of intersection points, number of loops,
number of hooks, point density, initial point position and
direction, end point position and direction, initial to end
 Sampling and geometric transformation is used
 Geometric transformation includes scaling, translation and
rotation
Features extraction

12.  The patter of the signature is created and stored
 The pattern is determined using signature based algorithm
Trained Pattern

13.  Step 1: create an empty signature
 Step 2: Get the values of co-ordinates of two points i.e.
start and end points from the input
 Step 3: Compute the slope of two points and hence angle of
deviation
 Step 3: Compare the angle and append their symbol to
 If angle If( degree>=0 and degree<45) and ( degree>=315
and degree<=360) assign “R” i.e. right to the signature
 If( degree>=45 and degree<135) then append ‘U’ to string
signature.
Signature based algorithm

14.  If( degree>=135 and degree<225) then append ‘L’ to string
signature
 If( degree>=225 and degree<315) then append ‘D’ to
string signature.
 Step 4: Go to 2 till end of input stroke
 Step 5: Return signature
Signature based algorithm

15. Example
For the above input stroke the signature will be: 1-2:D,2-
3:U,3-4:L,4-5:D,5-6:R,6-7:D
Hence signature is: DULDRD

16.  Recognition is the process of testing the input and identify
the desired output
 In recognition first pattern of the signature is created using
the signature based algorithm and best match is identified
using the k-nearest neighbor’s (KNN) algorithm.
Recognition

17.  k nearest neighbor is an non parametric method used for
classification and regression in pattern recognition.
 An object is classified by a majority vote of its neighbors,
with the object being assigned to the class most common
among its k nearest neighbors
 If k = 1, then the object is simply assigned to the class of
that single nearest neighbor, if k=2 then two nearest points
are assigned and so on.
k-NN algorithm

18. k-NN algorithm
Consider the following point distribution:
If the nearest neighbor of blue star is to be found than it can
be determined as:

19. k-NN algorithm
Considering k=3,
So the nearest neighbors of blue stars are three red circles.

20.  To measure the distance between points A and B in a feature
space.
 Euclidean distance function is the most widely used one to
calculate the nearest distance and given by the formula
Distance function

21.  Cosine similarity measure is typically used to calculate
similarity values between documents in text retrieval by
Distance function
where, the numerator represents the dot product of the
vectors A⃗ and B⃗
denominator is the product of their Euclidean lengths.

22.  Android Studio – IDE
 Programming language as Java
 MySQL database
Technology used

23.  Characters with modifiers can be implemented
 Recognition of words, lines, numbers and paragraph
 Can be used as a keyboard with simple gesture input
 Can be implemented in other different language
Future Recommendation

24. Results
Fig: A Front end UI design of the system

25. Results
Fig: Input test and recognition of consonant character “क”

26. Results
Fig: Training the consonant characters “ग” and “क्ष”

27. THANK YOU