this is the bangla handwriting digit proget proposal demo.

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DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
BANGLADESH ARMY UNIVERSITY OF SCIENCE & TECHNOLOGY (BAUST)
SAIDPUR CANTONMENT, NILPHAMARI
(Project Proposal)
Course Code: CSE 4132 Course Title: Artifacial Intelligence and Fuzzy Systems
Date: February 12, 2023
1. Name of the Students (with ID) :
 Abu Rayhan Mouno (180201118)
 Khondoker Abu Naim (200101103)
2. Present Address : Abbas Uddin Ahmed Hall,
Bangladesh Army University of Science & Technology
(BAUST),
Saidpur Cantonment, Saidpur, Nilphamari.
3. Name of the Department : Computer Science & Engineering
Program : Bachelor of Science in Computer Science and Engineering
4. Tentative Title : Bangla handwritten digit recognition using deep learning.
5. Introduction
Bangla handwritten digit recognition is a classical problem in the field of computer
vision. There are various kinds of practical application of this system such as OCR, postal
code recognition, license plate recognition, bank checks recognition etc. Recognizing
Bangla digit from documents is becoming more important. The unique number of Bangla
digits are total 10. So the recognition task is to classify 10 different classes. The critical
task of handwritten digit recognition is recognizing unique handwritten digits. Because
every human has his own writing styles. But our contribution is for the more challenging
task. The challenging task is about getting robust performance and high accuracy for
large, unbiased, unprocessed, and highly augmented NumtaDB dataset. The dataset is a
combination of six datasets that were gathered from different sources and at different
times containing blurring, noise, rotation, translation, shear, zooming, height/width shift,

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brightness, contrast, occlusions, and superimposition. We have not processed all kinds of
augmentation of this dataset. We have processed blur and noisy images mainly. Then our
processed image are classified by a deep convolutional neural network (CNN).
6. Background and Present State of the Problem
Bangla Handwritten Digit Recognition refers to the task of identifying and recognizing
handwritten digits in the Bengali language. It is an important application of image
recognition technology and has several practical applications in fields such as finance,
postal services, and document digitization.The recognition of Bangla handwritten digits is
a challenging problem due to the large variability in writing styles and the lack of
standardization in handwriting. Additionally, the Bangla script has a complex structure,
with several characters having multiple variations depending on their position within a
word.
Despite these challenges, there have been several efforts to develop robust and accurate
Bangla handwritten digit recognition systems. In recent years, deep learning-based
approaches have shown promising results in this area. One such approach is the use of
Convolutional Neural Networks (CNNs) for feature extraction and classification. Several
studies have shown that CNN-based models can achieve high accuracy rates in Bangla
handwritten digit recognition tasks. For example, a study published in 2020 reported an
accuracy of 99.03% on a benchmark dataset using a CNN-based approach. Another
promising approach is the use of Recurrent Neural Networks (RNNs) for recognizing
handwritten digits in Bangla. RNNs have the ability to model sequential data, which
makes them well-suited for handling the complex structure of the Bangla script.
Currently, there are several open-source datasets available for training and testing Bangla
handwritten digit recognition systems.
These datasets have helped to facilitate research in this area and have contributed to the
development of accurate and robust recognition systems. In conclusion, Bangla
Handwritten Digit Recognition is an important area of research with several practical
applications. Deep learning-based approaches, such as CNNs and RNNs, have shown
promising results in this area, and further research is needed to develop more accurate
and robust recognition systems.
7. Objective with Specific Aims and Possible Outcome
The primary objective of Bangla Handwritten Digit Recognition is to accurately identify
and classify handwritten digits in the Bengali language. This can be achieved through the
development of robust and accurate recognition systems that can process and interpret the
complex structure of the Bangla script.
Specific aims of Bangla Handwritten Digit Recognition may include:
 Developing deep learning-based models, such as CNNs and RNNs, to accurately
recognize handwritten digits in Bangla.

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 Improving the accuracy and robustness of existing recognition systems through
the use of advanced feature extraction and classification techniques.
 Developing benchmark datasets that accurately capture the variability in writing
styles and the complexity of the Bangla script, to facilitate research in this area.
 Investigating the impact of various factors, such as image quality, lighting
conditions, and writing styles, on the accuracy of Bangla handwritten digit
recognition systems.
 Exploring the potential applications of Bangla handwritten digit recognition in
various fields, such as finance, postal services, and document digitization.
Overall, the objective of Bangla Handwritten Digit Recognition is to develop accurate
and robust recognition systems that can process and interpret handwritten digits in the
Bengali language, and to explore the potential applications of this technology in various
fields.
8. Outline of Methodology Design
Dataset Preparation:
The first step is to prepare a dataset of handwritten digit. This dataset can be created by
collecting handwriting samples from different individuals. The dataset can include
different variations of each digit to improve the model's robustness. The dataset will be
split into training and testing sets.
Fig: Data preparation steps for the proposed method.
Preprocessing:
The next step is to preprocess the dataset. This step will include image resizing,
normalization, and noise removal. The preprocessed images will be used to train the deep
learning model.
Feature Extraction:

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Extract features from the preprocessed images. Popular feature extraction techniques
include Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform
(SIFT), and Local Binary Patterns (LBP).
Model Design:
The deep learning model will be designed using convolutional neural networks (CNNs).
CNNs are particularly suitable for image recognition tasks as they can learn and extract
features from the images. The model architecture will be optimized to achieve high
accuracy.
Model Training:
The model will be trained using the preprocessed dataset. The training process will
include optimizing the model's parameters using backpropagation and stochastic gradient
descent algorithms. The model will be trained until it reaches a satisfactory accuracy
level.
Model Evaluation:
The trained model will be evaluated on a separate test dataset. The evaluation process
will measure the model's accuracy and performance, including precision, recall, and F1
score.
User Interface:
Finally, a user interface will be developed to demonstrate the model's functionality. The
user interface will allow users to input a handwritten Bangla digit and display the
recognized character.
9. Resources Required to Accomplish the Task
 Python
 Kaggle
10. References
[1] Chakraborty, Partha, Syeda Surma Jahanapi, and Tanupriya Choudhury. "Bangla
Handwritten Digit Recognition." Cyber Intelligence and Information Retrieval:
Proceedings of CIIR 2021. Springer Singapore, 2022.
[2] Hossain, M. Zahid, M. Ashraful Amin, and Hong Yan. "Rapid feature extraction for
Bangla handwritten digit recognition." 2011 International Conference on Machine
Learning and Cybernetics. Vol. 4. IEEE, 2011.
[3] Fahim Sikder, Md. "Bangla handwritten digit recognition and
generation." Proceedings of International Joint Conference on Computational
Intelligence: IJCCI 2018. Springer Singapore, 2020.
[4] Hoq, Md Nazmul, et al. "A comparative overview of classification algorithm for
Bangla handwritten digit recognition." Proceedings of International Joint Conference on
Computational Intelligence: IJCCI 2018. Springer Singapore, 2020.

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[5] Rabby, AKM Shahariar Azad, et al. "Bangla handwritten digit recognition using
convolutional neural network." Emerging Technologies in Data Mining and Information
Security: Proceedings of IEMIS 2018, Volume 1. Springer Singapore, 2019.
Name and ID of the Students Signature of the Students
180201118
Abu Rayhan Mouno
200101103
Khondoker Abu Naim