srikarthikv/kannada-ocr-443-classes

Kannada OCR Model (443 Classes)

A deep learning model for Kannada character recognition using transfer learning with VGG16.

Model Details

• Architecture: VGG16-based transfer learning
• Classes: 443 unique Kannada characters
• Input Size: 128x128 RGB images
• Training Images: 4,642 samples
• Validation Images: 905 samples

Performance

• Validation Accuracy: 85.16%
• Top-5 Accuracy: 98.21%
• Training Time: ~3 hours 9 minutes on dual RTX 4090s

Model Architecture

VGG16 (frozen weights)
├── BatchNormalization
├── Flatten
├── Dense(1024, relu) + L2 regularization
├── BatchNormalization + Dropout(0.5)
├── Dense(512, relu) + L2 regularization
├── BatchNormalization + Dropout(0.4)
└── Dense(443, softmax)

Usage

Download Model from Hugging Face

from huggingface_hub import hf_hub_download
import tensorflow as tf
import pandas as pd

# Download model and class names
model_path = hf_hub_download(repo_id="srikarthikv/kannada-ocr-443-classes", filename="kannada_model_full.h5")
class_names_path = hf_hub_download(repo_id="srikarthikv/kannada-ocr-443-classes", filename="class_names.csv")

# Load model
model = tf.keras.models.load_model(model_path)

# Load class names
class_df = pd.read_csv(class_names_path)
class_names = class_df['class_name'].tolist()

Prediction

import cv2
import numpy as np

def preprocess_image(image_path):
    # Read and resize image
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (128, 128))

    # Normalize and add batch dimension
    img = img.astype(np.float32) / 255.0
    img = np.expand_dims(img, axis=0)

    return img

# Make prediction
image = preprocess_image("path/to/kannada_character.jpg")
predictions = model.predict(image)

# Get top prediction
top_class_idx = np.argmax(predictions[0])
confidence = predictions[0][top_class_idx]
predicted_character = class_names[top_class_idx]

print(f"Predicted Character: {predicted_character}")
print(f"Confidence: {confidence:.4f}")

Interactive UI

Run the Streamlit UI for testing:

pip install streamlit opencv-python
streamlit run test_model_ui.py

Files

• kannada_model_full.h5 - Complete trained model (168 MB)
• class_names.csv - Character class mappings
• test_model_ui.py - Interactive Streamlit UI for testing

Training Details

• Framework: TensorFlow/Keras
• Base Model: VGG16 (ImageNet weights, frozen)
• Optimizer: Adam (lr=0.001)
• Loss: Categorical Crossentropy
• Batch Size: 64
• Epochs: 60
• Data Augmentation: Rotation, shift, shear, zoom, brightness
• Class Weights: Balanced for imbalanced data

Model Card

Metric	Value
Model Size	168 MB
Parameters	17.3M total (2.6M trainable)
Training Time	3h 9m
GPU Memory	~12GB (dual RTX 4090)
Inference Time	~10ms per image

Hugging Face Repository

🤗 Model: srikarthikv/kannada-ocr-443-classes

License

This model is released under MIT License for research and educational purposes.

Citation

@misc{kannada-ocr-443,
  title={Kannada OCR Model with 443 Character Classes},
  author={Your Name},
  year={2025},
  url={https://huggingface.co/srikarthikv/kannada-ocr-443-classes}
}