This document describes a project on Bangla handwritten digit recognition using deep learning models. It discusses preprocessing a dataset of 2500 training and 500 testing Bangla handwritten digit images. Two models - EfficientNetB0 and MobileNet - were trained using baseline, transfer learning, and fine-tuning methods. Fine-tuning achieved the best results, with 99.4% and 94% accuracy for EfficientNetB0 and MobileNet respectively. Limitations and future work are discussed to improve dataset quality and model performance.

1. Bangladesh Army University of
Science and Technology,Saidpur
 Course Title: Artifacial Intelligence and
Fuzzy Systems
 Course Code: CSE 4132
 Level-4;Term-1
 Course Adviser:Hasan Muhammad
Kafi

2.  Abu Rayhan Mouno (180201118)
 Khondoker Abu Naim (200101103)
2
Project Title: Bangla handwritten digit
recognition using deep learning.
 Peresented by,

3.  Introduction
 Bangla Handwritten Characters
Proposed Methodology
 Dataset Discription
 Preprocessing
 Methodology
 Implemented Model Architecture
 Training The Network
 Results Analysis
 Limitation
 Future Recomendation
 Conlusion
3
Contents

4. 4
Introduction
Handwritten digit recognition is a
classical problem in computer vision,
with 10 unique Bangla digits to classify.
The models evaluated are:
 EfficientNetB0
 MobileNet.
These models' performances are
contrasted under various training
regimes. We are using baseline,
transfer learning, and fine-tuning.

5. )
Bangla Handwritten Digits
Handwritten digits have unique characteristics such as
strokes, styles, and structure
Different people’s digits are unique to each person
Bangla handwritten digits have a morphologically complex nature

6. PROPOSED METHODOLOGY
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Data Collection &
Preprocessing
Dataset Generation
Training
Classification using
CNN
Optimization
Testing

7. Dataset Discription
7
 We have used our own dataset- 10 different Bangla
handwritten digits.
 2500 images for the Training and 500 images for the Testing
Datasets
 10 folders for the both Training and Testing Datasets,
denoting 10 digits' classes
 Each folders contain an equal number of sample images

8. Dataset Description
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০ ১ ২ ৩ ৪ ৫ ৬ ৭ ৮ ৯
200 220 250 250 203 267 223 231 224 245
Class
Train Set
Validatio
n
Set
69 71 75 73 70 75 73 60 65

9. The Dataset(Cont.)
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The training dataset was further split
into two groups:
 Training Dataset (90% of the
original training dataset)
 Testing Dataset (10% of the
original training dataset) -
Ensuring no bias was enforced
in Testing
0
500
1000
1500
2000
2500
3000
Training Dataset Testing Dataset
Dataset
Fig1.Dataset chart

10. The Dataset(Cont.)
10
Fig2.Sample of images from dataset Fig3.sample of images from result

11. Methodology: Preprocessing
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• Image were converted into
grayscale images
• All images were resized into a
124x124 dimension to target size
10 Kilobytes
Fig4. 124x124 resized images of digit “9”

12. Methodology:Environment
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Resources Required to Accomplish the Task:
• Python
• Kaggle
Installation of all necessary tools and libraries, such as
TensorFlow, Keras, and NumPy, in both settings.

13. Methodology:Model Architecture
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Figure5. An illustration of the structure of our model training
• We are using pre-trained deep learning models including
EfficientNetB0 and MobileNet.
• We compared this models under different training regimes including
baseline, fine-tuning and transfer learning.
Input
124x124x1 124x124x64 124x124x64 14x14x128 7x7x256 7x7x256 7x7x256

14. TRAINING THE NETWORK
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• Weights were initialized with Xavier initialization method
• The network was trained all over using Adam optimizer
with initial standard parameters
• The model was trained using batches of size 32
• We used LR Scheduler for reducing the learning rate
when validation loss was not improving
• Total iteration to train the model: 16

15. Result and analysis: overview of performance
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Model Baseline Transfer
learning
Fine-tuning
EffNetB0 31% 95% 99.4%
MobileNet 9% 92% 94%
31
9
99.4
94
95 92
0
20
40
60
80
100
120
EffNetB0 MobileNet
Result Analysis
Baseline Fine-tuning Transfer learning

16. 16
Limitations
• Sample size: This dataset's sample size was limited, which might have
had an impact on the statistical power and generalizability of the results.
• Data quality: Data quality was determined by accuracy and
comprehensiveness of participant replies.
• Generalizability: Due to the sample being restricted to a particular
participant group, it is possible that the results of this study cannot be
applied to other populations or circumstances.

17. 17
Future recommendations
• Examine other pre-trained models:Explore different Neural Network
models Work with various datasets to create a more robust and effective
automated system.
• Improve dataset quality
• Improve dataset quality
• Dataset Quality:Increase the amount of high-quality data was
collected, especially for underrepresented classes, to improve the
precision of machine learning models.
• Try different optimizer algorithoms:Try out several
optimization techniques, such RMSprop or SGD to boost the
effectiveness of the digit recognition system.
• Extend to real-time applications:Extend current implementation
to real-time applications for practical use.

18. Conclusion
• Created models for recognizing digit
using a variety of deep learning
architectures and training methods.
• As far as our knowledge, the achieved
result is the state-of-the art accuracy in
Bangla digit recognition.
• Could result in advancement in the
pursuit of digitization.
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19. “
”
Thank You
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