NNI Pinyin Hand-writing Recognition Model

Handwritten Chinese Pinyin Recognition with Neural Networks & MySQL

In today’s data-driven world, leveraging the power of neural networks to make precise predictions is crucial. Dive into this exciting project where I seamlessly integrate a robust Convolutional Neural Network (CNN) with a MySQL database to recognize and categorize handwritten Chinese Pinyin images.

One challenge of recognizing handwriting PingYing that distinguishes it from digit or alphabet letters is the use of Pinyin tone (accent: high ā, rising á, falling-rising ǎ, and falling à) which is often times displayed as a line pattern indicator on the top of the vowel.

1. Database Creation and Structuring

The foundation of the project is a robust MySQL database, structured to store and manage handwritten images of Chinese Pinyin. Within the database, a dedicated table is provisioned, where each record consists of a unique identifier (like image_id) and its corresponding image data, stored in BLOB format. Efficiently indexed and organized, this setup ensures that images can be swiftly retrieved and processed, laying the groundwork for the successive machine learning pipeline.

2. Image Data Retrieval and Preprocessing

Using Python’s MySQL connectors, batches of images are extracted for analysis. Once fetched, each image undergoes a series of preprocessing steps essential for ensuring consistent neural network input. These transformations, implemented using libraries like torchvision, include converting the image to grayscale, resizing it to a consistent dimension (e.g., 28x28 pixels), and normalizing its pixel values. This standardized input is now primed for the neural network, ensuring optimal recognition accuracy.

# Connect to the database
conn = mysql.connector.connect(user='***', password='***', host='***', database='***')
cursor = conn.cursor()

# Fetch a batch of images
batch_size = 50
# Since the whole database is built locally inside my computer, I only fit 50 sample pictures.
# A larger sample size with more training will result in better performance

cursor.execute("SELECT image_id, image_data FROM images_table LIMIT %s", (batch_size,))
image_batch = cursor.fetchall()

# Convert BLOB data to images
images = [Image.open(BytesIO(image_data)) for _, image_data in image_batch]

3. Neural Network Inference and Recognition

At the heart of the system is a Convolutional Neural Network (CNN) tailored for handwritten Pinyin recognition. After training this neural network on a labeled dataset, the model is set to “evaluation” mode, ready to make predictions on new, unseen data. The preprocessed images are then passed through this network, yielding predictions that classify each image into its corresponding Pinyin category. The powerful CNN architecture ensures that even nuanced differences in handwriting styles are discerned, leading to accurate recognition.

# Define the layers of the CNN
        self.conv_layer = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        # Fully connected layers
        self.fc_layer = nn.Sequential(
            nn.Linear(64*6*6, 1000),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(1000, num_classes)
        )

Defines a basic Convolutional Neural Network in PyTorch, comprising sequential convolutional layers to extract features from images and fully connected layers to classify these features. Use forward propagation dictates the flow of input data through these layers. The network architecture includes activation functions, pooling, and dropout mechanisms for non-linearity, dimensionality reduction, and regularization respectively.

...
def forward(self, x):
        x = self.conv_layer(x)
        x = x.view(x.size(0), -1)  # Flatten for the fully connected layer
        x = self.fc_layer(x)
        return x

4. Results Utilization and Storage

NNP1

Post-inference, the project doesn’t just stop at recognition. The obtained Pinyin predictions can be stored back into the MySQL database, linked to their corresponding image IDs, enabling easy retrieval and analysis in the future. Alternatively, these results can be channeled into other systems or applications, whether it’s for displaying them in a user interface, generating linguistic reports, or any other domain-specific utility. This holistic approach ensures that the insights gleaned from the neural network translate into tangible, actionable outcomes.

Please feel free to contact me if you would like to learn more about this exciting project or if you have any inquiries related to my skills and experience.