Handwritten digit recognition is a
classical problem in computer vision,
with 10 unique Bangla digits to classify.
The models evaluated are:
These models' performances are
contrasted under various training
regimes. We are using baseline,
transfer learning, and fine-tuning.
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
7. Dataset Discription
We have used our own dataset- 10 different Bangla
2500 images for the Training and 500 images for the Testing
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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200 220 250 250 203 267 223 231 224 245
69 71 75 73 70 75 73 60 65
9. The Dataset(Cont.)
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
Training Dataset Testing Dataset
13. Methodology:Model Architecture
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.
124x124x1 124x124x64 124x124x64 14x14x128 7x7x256 7x7x256 7x7x256
14. TRAINING THE NETWORK
• 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
Model Baseline Transfer
EffNetB0 31% 95% 99.4%
MobileNet 9% 92% 94%
Baseline Fine-tuning Transfer learning
• 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.
• Examine other pre-trained models:Explore different Neural Network
models Work with various datasets to create a more robust and effective
• 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.
• 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.