Handwriting Recognition Using Deep Learning and Computer Version
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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.
![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].](https://image.slidesharecdn.com/presentation-190721130429/85/Handwriting-Recognition-Using-Deep-Learning-and-Computer-Version-12-320.jpg)
Handwriting Recognition Using Deep Learning and Computer Version
- 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
- 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…