Handwriting Recognition

This project focuses on developing a system for recognizing text in handwritten images. It involves preprocessing the data to convert text labels into a numerical format and training neural networks for text recognition tasks.

Key Components:

Data Label Preprocessing:

• Character Set and Constants: Define the set of valid characters, maximum input label length, and other constants.
• Label-to-Number Conversion: Convert text labels into a numerical representation for training.
• Number-to-Label Conversion: Convert numerical representations back into text labels for evaluation.

Convolutional Recurrent Neural Network (CRNN) Architecture:

• Input Layer: Accept grayscale images with dimensions of 256x64 pixels.
• Convolutional Layers: Apply convolutional filters and pooling operations for feature extraction.
• Reshape Layer: Reshape the output of convolutional layers for sequence processing.
• Bidirectional LSTM Layers: Utilize bidirectional LSTM layers for sequence modeling.
• Output Layer: Output layer with softmax activation for character prediction.

Training and Evaluation:

• CTC Loss Calculation: Compute the Connectionist Temporal Classification (CTC) loss between predicted and true labels.
• Training the Model: Train the CRNN model using preprocessed data and evaluate its performance.
• Evaluation Metrics: Assess the model's performance using character-level accuracy and other metrics.

Project Goals:

• Achieve high accuracy in recognizing handwritten text.
• Develop a robust system capable of handling various handwriting styles and conditions.
• Explore techniques for improving model performance and efficiency.

Usage:

1. Data Preprocessing: Preprocess handwritten images and convert text labels into a numerical format.
2. Model Training: Train the CRNN model using preprocessed data.
3. Evaluation: Evaluate the trained model's performance using evaluation metrics.
4. Prediction: Use the trained model to predict text in new handwritten images.

Dependencies:

• Python (>=3.8)
• TensorFlow
• NumPy
• OpenCV
• Matplotlib
• Pandas

Repository Structure

• load_data: Scripts or classes for loading training and validation images.
• clean_data: Scripts for cleaning data, including replacing inappropriate data or deleting.
• preprocessing: Scripts for preprocessing data.
• prepare_dataset: Scripts for preparing the dataset, including generating output, calculating lengths of dates, and other tasks.
• model: Implementation of the CRNN model architecture.
• data_loader: Scripts or classes for loading data.
• training: Scripts for model training and evaluation.
• testing: Scripts for testing the model on validation and test datasets.
• analysis_and_model_adjustments: Scripts for analyzing model performance, identifying unrecognized characters, and making adjustments to improve model performance.

Documentation

• README.md: Contains a comprehensive project description, instructions for running, project dependencies, and usage examples.
• requirements.txt: List of all project dependencies.

Start Model

• start_model.ipynb Notebook to start model. Each cell should be executed one by one until the end.
• project_train_model.ipynb Notebook to train model.