The document discusses various threshold methods for handwritten Marathi character recognition, detailing global and local thresholding techniques and their effectiveness. It highlights issues associated with thresholding, such as noise and pixel reliability, and compares multiple methods including Otsu's multi-level thresholding. The study concludes that local thresholding is effective for segmenting handwritten text images and suggests potential for feature extraction improvements.

1. Presented by: Vrushali T. Lanjewar
Paper ID- 130
Comparison of Threshold Methods useful in
Handwritten Marathi Character
Recognition
Authors: R. N. Khobragade1, Vrushali T. Lanjewar2, Mahendra S. Makesar3
1,2 Sant Gadge Baba Amravati University, Amravati, India,
3Nagpur Institute of Technology, RTMNU, Nagpur, India.
1st International virtual Conference on Integrated Intelligence Enable Networks
& Computing (IIENC-2020)

2. Contents
 Introduction
 Problems
 Methods
 Result and Discussion
 Conclusions

3. Introduction
If the image is 𝑔 𝑥, 𝑦 Then, the threshold of 𝑔 𝑥, 𝑦 as global threshold 𝑇
given by equation (1).
𝑔 𝑥, 𝑦 = ቊ
1, 𝑖𝑓𝑓 𝑥, 𝑦 ≥ 𝑇
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
………….. (1)
Thresholding generates a binary image using all pixels below a certain
threshold set to zero and all the pixels above the threshold set to one [1].
Original
image
Threshold
image

4. Problem using thresholding of image
➢The main problem in thresholding is while processing the image, we
only focus on intensity and no correlation between pixels considered.
➢In the initial work, there is no surety that the fixed pixels are reliable.
We add or separate external pixels that are not part of the desired
pixels in the region, especially near the state boundary [2].
➢As the noise increases, so does the effect also increase. This is
because the pixel intensity does not reflect the intensity of the field.
Problems

5. Methods
(a) Threshold for iteration 1=0.4117,
(b) Threshold for iteration 2=0.4738
(c) Threshold for iteration 3=0.4858,
(d) Threshold for iteration 4=0.4898
1. Global Threshold using iteration
Liu Dong et al. (2008), proposed an iterative
algorithm to give maximum thresholds from
image histograms.
Fig.1. a) Gray image with Histogram corresponding b) binary image and Global threshold

6. Step1: Read the input gray-scale image
Step2: Compute and display histogram of input image
Step3: Max and Min of Input
Step4: Initialize Iteration loop
 count =1;
Step5: Calculate Binary image using threshold Thresh, set value to calculate
threshold. d is set to false to create iterations.
Step6: Generate Binary image using threshold
Step7: Calculate a New temporary threshold value
Step8: Increment iteration and save the values of ‘Thresh’ in the ‘save’ array

7. 2. Multi-level threshold using Otsu method
Diego Oliva et al. (2013), present the multi-level threshold based on Harmony
Search Optimization.
Fig.2. Multi-level thresholding on gray-scale image using Otsu’s fitness function and
corresponding histogram of the best fitness plot for image 1

8. Results
Sr.
no
Threshold
type
Method MSE PNSR Jaccard Distance
1 Global
Threshold
Otsu’s [1] 2.9989e+04 3.3612 0.1902
2 Kapur’s [2] 0.01920000 65.2977 0.0235
3 Local
Threshold
Niblack [3] 2.9998e+04 3.3599 0.2205
4 Bernsen [4][5] 2.9979e+04 3.3626 0.2544
5 Sauvola [6] 2.9946e+04 3.3673 0.0645
6 Feng [7] 2.9981e+04 3.3623 0.1171
7 Wolf [8][13] 2.9970e+04 3.3639 0.0686
8 Bradley [9] 3.0027e+04 3.3556 0.2938
9 Adaptive [11][12] 2.9926e+04 3.3703 0.2260
10 Hysteresis [13] 2.9971e+04 3.3637 0.0962
11 Varied threshold[15] 2.9858e+04 3.3802 0.2313
Table 1. Comparison of Threshold of Image using various methods

9. Fig.3. Output image for Marathi compound character using
various threshold

10. Conclusions
In this paper, we study of global and local threshold performed to extract the text
foreground pixels in image.
Global thresholding is appropriate only when object and background classes are
distinctive.
Local image thresholding is useful for segmentation of handwritten text images.
Multi-level thresholding using Otsu’s method requires maximizing the class difference
between the background and the object.
There is scope that these used in feature extraction with multi-level thresholding and
global iterative partition method.

11. Acknowledgment
The authors acknowledge the financial assistance of the
Department of Science and Technology, Science and Engineering
Research Board (DST- SERB), New Delhi, Government of India,
under grant EEQ/2017/000102.

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13. Thank you