RESULTS.md

OCR Model Training and Selection Report

1. Objective

The goal of this experiment was to train and select the optimal Convolutional Neural Network (CNN) architectures for a Mixture of Experts (MoE) OCR system. The system required three specialized "expert" models: one for digits (0-9), one for uppercase letters (A-Z), and one for lowercase letters (a-z).

2. Methodology

Dataset

The EMNIST (byclass) dataset was used, which contains over 800,000 standardized 28x28 grayscale images of digits, uppercase, and lowercase letters.

Architectures Tested

Three different CNN architectures were evaluated for each expert task:

• CNNModel_Small: A baseline model with 2 convolutional layers and 1 fully-connected layer.
• CNNModel_Medium: A larger model with 3 convolutional layers and a wider fully-connected layer to capture more complex features.
• CNNModel_Large: A model with the same convolutional base as Medium but with a deeper, multi-layer classifier.

Experiment

To ensure the reliability of the results, the full training and evaluation process was conducted twice. The model that achieved the highest validation accuracy and lowest validation loss was selected for each expert category. Early stopping was used based on validation loss to prevent overfitting.

3. Results Summary

The following tables show the best performance (validation accuracy and loss) achieved by each architecture across the two independent runs.

Run 1 Results

Architecture	Expert	Best Val Accuracy	Best Val Loss
small	digits	99.60%	0.0156
small	uppercase	97.97%	0.0770
small	lowercase	95.84%	0.1412
medium	digits	99.51%	0.0180
medium	uppercase	98.31%	0.0618
medium	lowercase	95.94%	0.1338
large	digits	99.58%	0.0184
large	uppercase	98.22%	0.0706
large	lowercase	95.88%	0.1381

Run 2 Results

Architecture	Expert	Best Val Accuracy	Best Val Loss
small	digits	99.51%	0.0165
small	uppercase	98.13%	0.0779
small	lowercase	95.39%	0.1520
medium	digits	99.59%	0.0182
medium	uppercase	98.27%	0.0679
medium	lowercase	96.16%	0.1279
large	digits	99.54%	0.0195
large	uppercase	98.17%	0.0699
large	lowercase	96.00%	0.1302

4. Final Model Selection

Based on the analysis of the results, the following models were chosen for the final OCR system.

Expert Category	Selected Architecture	Justification
Digits	Small	Consistently achieved the highest accuracy (~99.6%) and lowest loss. Larger models offered no benefit.
Uppercase	Medium	Outperformed the Small model and achieved the highest accuracy (98.31%).
Lowercase	Medium	Highest accuracy (96.16%) and lowest loss, superior for more complex letter shapes.

These selected models will be used for the implementation of the character recognition pipeline.