Handwritten character recognition, frequently known as Optical Character Recognition (OCG) for handwritten text, is a difficult issue due to the variability in the individual handwriting styles. Neural networks, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs) have been increasingly efficient in this field. Getting a strong thesis statement is a crucial task our professional thesis writers help you to go through all the odds by sharing novel ideas. Here we give a step-by-step guidance to execute handwritten character recognition by employing a Neural Network:

1. Data Collection:

• Dataset: For handwritten digit recognition, the Modified National Institute of Standards and Technology (MNIST) dataset is supported by us. For more prolonged characters, our model utilizes the EMNIST (Extended MNIST) datasets that involve letters as well.

2. Data Pre-processing:

• Normalization: For normalization, our project scales the pixel values from [0,255] to [0, 1] by dividing 255.0.
• Reshaping: In data preprocessing, we require a reshape of 2828 pixel image into 28 1 (adding a channel dimension), when employing a CNN.
• One-hot Encoding: For classification, our model uses one-hot encoding by labeling 0-9 for digits and more for characters.

3. Model Building:

• Using a CNN: For image data CNNs are general. Some of the basic architectures involves are:
• Convolutional Layers (with activation, e.g., ReLU)
• Pooling Layers like Max pooling
• Fully connected layers like dense layers
• Output layer with softmax activation, when it is 10 neurons for digits and if it is more we identify characters as well.
• Using a RNN (like LSTM): LSTM is valuable for consecutive handwriting like cursive. From handwritten lines, the LSTMs can process sequences of image slices and identify consecutive characters.

4. Model Training:

• Loss Functions: For multi-class classification, we employ categorical cross entropy technique.
• Optimizer: For good initial points, our model incorporates Adam, RMSprop, or SGD.
• Batch Size and Epochs: Our research initiates with a small batch size like 32 and that improves as required. Till the validation accuracy plateaus, we train the number of epochs.
• Validation Split: To monitor overfitting, employ a segment of the training data like 10% for validation during training.

5. Evaluation:

• Accuracy: By using a test set we estimate accuracy. For MNIST, the top efficiency frameworks perform over 99% accuracy.
• Confusion Matrix: Our framework frequently confuses others, and helps to find the characters.

6. Post-processing:

• Thresholding: For utilizing confidence thresholding is helpful for us, by only accepting forecasting if the model is necessarily confident.
• Dictionary Matching: To make sure that the identified sequences correspond with real words in the language otherwise we choose the corresponding word.

7. Deployment:

            If the model is fulfilled for us, we deploy the framework for real-world handwritten character identification. By taking into account our model, we use the platforms like TensorFlow Lite or ONNX for on-device deployment.  

Tips:

1. Data Augmentation: To create more training samples, our model improves the variability in training data by applying random transformations like rotation, scaling and shifting.
2. Complex Architectures: For identifying entire handwritten words or sentences, we look into most complicated architectures such as the mixture of CNNs and RNNs.
3. Transfer Learning: Pretrained frameworks can be employed on relevant tasks as an initial point and fine tune them for handwritten recognition.
4. Regularization: Our work employs dropout or L2 regularization to avoid overfitting.

            Recall that while for handwritten character recognition, neural networks are highly efficient, performing increased accuracy that needs a lot of fine-tuning and experimentation with the architecture, training process, and post-processing steps.