|
|
--- |
|
|
license: mit |
|
|
language: |
|
|
- bn |
|
|
tags: |
|
|
- image-classification |
|
|
- handwritten-character-recognition |
|
|
- bangla |
|
|
- computer-vision |
|
|
- efficientnet |
|
|
model-index: |
|
|
- name: Bangla Handwritten Character Recognition |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
# Model Card for Bangla Handwritten Character Recognition |
|
|
|
|
|
This is a Bangla handwritten character recognition model that can detect 195 classes, including compound characters. The model is designed for accurate offline recognition of Bangla handwritten script. |
|
|
|
|
|
## Model Detail |
|
|
Architecture: EfficientNetV2S (feature extractor) |
|
|
|
|
|
Custom Layers: Triple-head architecture with two attention mechanisms, followed by a soft average ensemble layer. |
|
|
|
|
|
Parameters: |
|
|
Total: 30,912,695 |
|
|
|
|
|
Framework: TensorFlow / Keras |
|
|
|
|
|
Finetuned from: EfficientNetV2S pretrained on ImageNet |
|
|
|
|
|
Developed by: Team Segfault |
|
|
|
|
|
License: MIT |
|
|
|
|
|
|
|
|
### Model Description |
|
|
|
|
|
This model is a deep learning-based handwritten character classifier designed specifically for the Bangla language. It recognizes 195 different Bangla characters, including basic vowels and consonants as well as compound letters. |
|
|
|
|
|
Built using the EfficientNetV2S architecture as a feature extractor, the model applies a custom triple-head fully connected (FC) architecture with two attention mechanisms to enhance discriminative power. The outputs from all three heads are averaged using a soft-voting strategy to produce the final prediction, improving generalization and reducing overfitting. |
|
|
|
|
|
The goal of this model is to support accurate offline recognition of handwritten Bangla text — useful for educational tools, digitization of documents, and OCR applications focused on Bangla script. It achieves high accuracy on a balanced dataset, thanks to a carefully designed data augmentation pipeline that includes ElasticTransform and random rotation. |
|
|
|
|
|
This model was trained on the MatrivashaBangla dataset(a newly combinded dataset), containing over 500,000 samples. The entire training and evaluation pipeline is implemented using TensorFlow/Keras. |
|
|
|
|
|
|
|
|
- **Developed by:** [Team Segfault] |
|
|
- **Model type:** [Deep Neural Network] |
|
|
- **License:** [MIT] |
|
|
- **Finetuned from model :** [EfficientNetV2S] |
|
|
|
|
|
|
|
|
## Uses |
|
|
1. Offline OCR systems for Bangla script |
|
|
2. Educational software or Bangla digitization tools |
|
|
3. Document recognition systems, etc |
|
|
|
|
|
### Out-of-Scope Use |
|
|
1. Not intended for real-time inference on low-power devices |
|
|
2. Not designed for other scripts or languages |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Data |
|
|
Dataset |
|
|
Name: MatrivashaBangla (a newly combinded dataset using BanglaLekha-Isolated and Matrivasha_raw) |
|
|
|
|
|
Size: 195 classes, ~500,000 images |
|
|
|
|
|
Augmentations: ElasticTransform, Rotate |
|
|
|
|
|
Balance: Class-balanced via custom augmentation scripts (2500-2580 images per class) |
|
|
|
|
|
|
|
|
## Evaluation |
|
|
|
|
|
Train Accuracy: ~97.59% |
|
|
|
|
|
Validation Accuracy: ~96.69% |
|
|
|
|
|
Metrics: Accuracy, F1-score, Precision, Recall |
|
|
|
|
|
Evaluation Dataset: Test split from MatrivashaBangla (10%), Accuracy ~96.60% |
|
|
|
|
|
### Model Architecture |
|
|
EfficientNetV2S (Frozen Layers) |
|
|
↓ |
|
|
Feature Output (Shared) |
|
|
↓ |
|
|
GlobalAvaragePooling |
|
|
↓ |
|
|
├── FC Head 1 with Attention |
|
|
├── FC Head 2 with Attention |
|
|
└── FC Head 3 (Baseline) |
|
|
↓ |
|
|
Soft Average of 3 Head Outputs |
|
|
↓ |
|
|
Final Prediction (195 classes) |
|
|
|
|
|
|
|
|
## Limitations |
|
|
1. May misclassify extremely poor handwriting or characters written with noisy backgrounds |
|
|
2. Not tested for real-time edge deployment |
|
|
3. Only trained on standard handwritten script — no cursive, artistic, or stylized forms |
|
|
|
|
|
## Environmental Impact |
|
|
- **Compute Region:** [Local training, not on cloud] |
|
|
- **Carbon Emitted:** [ ~5–10 kg CO2] |
|
|
|
|
|
## Model Card Contact |
|
|
Author: Meharaz Hossain |
|
|
|
|
|
Email: meharaz733@gmail.com |
|
|
|
|
|
GitHub: https://github.com/meharaz733 |
|
|
|
|
|
Hugging Face: https://huggingface.co/meharaz733 |
