This document presents a method for handwriting recognition using deep learning and computer vision. It discusses preprocessing images by removing noise and converting to grayscale. Thresholding is used to separate darker text pixels from lighter background pixels. The image is then segmented into individual lines and words. Python libraries like TensorFlow, Spyder and Jupyter Notebook are used. The goal is to build a system that can recognize text in images and display the text to users. Future work may include recognizing cursive text and additional languages.

1. Handwriting Recognition Using Deep Learning and Computer Version
Presenter
1. Naiyan Noor (150201018)
2. Md.Delwar Hosen Chowdhury(150201002)
3. Isfat Zahan Nila (150201020)
4. Horidash Chandro Roy(150201011)
Supervisor
Dr. Mohammed Sawket Ali
Asst. Professor
Department of Computer Science &
Engineering
Department of Computer Science and Engineering
Bangladesh Army University of Science and Technology

2. Presentation Outlines
Introduction
Objective
Outline Methodology Design
Conversion to Gray-Scale
Pre-Processing
Thresholding
Resource Required to Accomplish the Task
Image Segmentation
Conclusion
References

3. Introduction
 Handwriting recognition is the ability of a computer or device to take as input
handwriting from sources such as printed physical documents, pictures and other
devices, or to use handwriting as a direct input to a touchscreen and then interpret
this as text
 There are many are many fields in which deep-learning is being As used.
Lorem ipsum dolor sit amet, consectetur
adipiscing elit.
Lorem ipsum dolor sit amet,
consectetur adipiscing elit.
65%
50%
80%
35%
 Handwriting recognition is a challenging task because of many reasons.
 The primary reason is that different people have different styles of writing.
 The secondary reason is there are lot of characters like Capital letters, Small
letters, Digits and Special symbols.

4. Objective
 Study and Implement the different method
Computer Vision and Deep learning
 Implement the developed method using
python language
Lorem ipsum dolor sit amet,
consectetur adipiscing elit.
Lorem ipsum dolor sit amet,
consectetur adipiscing elit.
65%
50%
80%
35%
 To provide an easy user interface to input the
object image
 User should be able to upload the image
 System should retrieve text present in the
image and display them to the user

5. Outline Methodology Design

6. Pre-Processing
 This is the first step performed in image processing.
 In this step the noise from the image is removed by using
median filtering.
 Median filtering is one of the most widely used noise reduction
technique.
 This is because in median filtering the edges in image are
preserved while the noise is still removed.

7. Conversion to Gray-Scale
 After the pre-processing step, the image is converted
into grayscale.
 Conversion into grayscale is necessary because
different writers use pens of different color's with
varying intensities.
 Also working on grayscale images reduces the overall
complexity of the system.

8. Thresholding
 When an image is converted into grayscale, the handwritten
text is darker as compared to its background.
 With the help of thresholding we can separate the darker
regions of the image from the lighter regions.
 Thus because of thresholding we can separate the handwritten
text from its background.

9. Image Segmentation
 A user can write text in the form of lines.
 the thresholder image is first segmented into
individual lines.
 Then each individual line is segmented into individual
words. a

10.  Standard data set
 Python language
 Spyder and Jupiter Notebook
 PyCharm (IDE)
Resource Required to Accomplish the Task

11. Conclusion
There are many developments possible in this system in the
future. As of now the system can’t recognize cursive handwritten
text. But in future we can add support for recognition of cursive
text. Currently our system can only recognize text in English
languages. We can add support for more languages in the future.
Presently the system can only recognize letters and digits. We can
add support for recognition of Special symbols in the future. There
are many applications of this system possible. Some of the
applications are Processing of cheques in Banks, helping hand in
Desktop publishing, Recognition of text from business cards,
Helping the blind in recognizing handwritten text on letters.
.

12. References
[1] Wei Lu, Zhijian Li,Bingxue Shi . ” Handwritten Digits Recognition with Neural Networks and Fuzzy Logic” in IEEE International
Conference on Neural Networks, 1995. Proceedings.
[2] B. V. S. Murthy.” Handwriting Recognition Using Supervised Neural Networks” in International Joint Conference on Neural
Networks, 1999. IJCNN ’99.
[3] M. Gilloux, J.-M. Bertille, and M. Leroux, “Recognition of Handwritten Words in a Limited Dynamic Vocabulary,” Third Int’l
Workshop Frontiers in Handwriting Recognition, pp. 417–422, CEDAR, State Univ. of New York at Buffalo, May 1993.
[4] S. Edelman, S. Ullman, and T. Flash, “Reading Cursive Script by Alignment of Letter Prototypes,” Int’l J. Computer Vision, vol. 5,
no. 3, pp. 303–331, 1990.
[5]” An open-source machine learning framework for everyone” https://www.tensorflow.org/, [Online] Available:
https://www.tensorflow.org/. [Accessed 05 March 2018].

13. Any Question?
Handwriting Recognition
Using Deep Learning and
Computer Version

14. Thank You…