sjvasquez/handwriting-synthesis
Handwriting Synthesis
Este proyecto es un generador de scripts neuronales que utiliza una red neuronal recurrente para sintetizar escritura a mano similar a la humana. Mapea caracteres de texto ASCII a coordenadas realistas de trazos de pluma a través de un mecanismo de atención para imitar patrones de escritura naturales.
El sistema permite la personalización del estilo de escritura ajustando los parámetros de cebado y sesgo para controlar la pulcritud y las características estilísticas del texto generado. Los usuarios también pueden definir el formato de salida, incluyendo colores de trazo y grosores de línea, para los scripts digitales resultantes.
El proyecto incluye un flujo de trabajo completo de entrenamiento de redes neuronales, con un pipeline de datos que normaliza los datos de trazos XML y las transcripciones ASCII en matrices numéricas. La infraestructura de entrenamiento cubre el recorte de gradientes, el control de puntos de control basado en validación y el registro de métricas para monitorizar la precisión y la pérdida.
Features
- Recurrent Neural Networks - Utilizes a recurrent neural network to generate sequences of pen stroke coordinates for human-like writing.
- Input Sequence Attentions - Implements attention mechanisms to weight input character positions during the synthesis of handwriting strokes.
- Handwriting Synthesis Models - A deep learning model that maps characters to handwriting sequences to mimic natural writing styles.
- Prefix Bias Control - Provides control over output neatness and style by adjusting priming and biasing parameters during generation.
- Handwriting Stroke Transformers - Implements a data pipeline that parses XML stroke data and ASCII text into normalized numerical arrays.
- Stroke Coordinate Normalizations - Normalizes XML stroke data and ASCII transcriptions into numerical arrays suitable for handwriting model training.
- Text-to-Handwriting Synthesis - Synthesizes realistic pen stroke coordinates from text to simulate natural human handwriting.
- Stroke Sequence Generators - Generates coordinates and pen strokes by processing nested states through a recurrent neural network.
- Synthesis Style Controllers - Adjusts the neatness and stylistic characteristics of generated text by modifying biasing and priming parameters.
- Synthesis Style Parameters - Adjusts the neatness and stylistic characteristics of synthesized text via priming and biasing parameters.
- Gradient Clipping Utilities - Uses gradient clipping during the training loop to maintain stability and prevent exploding gradients in recurrent layers.
- Validation-Based Checkpoint Selection - Saves and restores model checkpoints based on the lowest recorded validation loss to ensure optimal model selection.
- Best Model Checkpointing - Automatically saves the best model state based on validation loss metrics during the training process.
- Training Execution Loops - Implements a training execution loop that manages parameter updates, gradient clipping, and loss monitoring for the RNN.
- Recurrent Neural Network Training - Manages the training workflow of a recurrent neural network to learn specific handwriting patterns.
- Handwriting Visual Renderers - Converts ASCII text into visual handwriting sequences with customizable stroke colors and line widths.
- Dataset Processors - Processes XML stroke data and ASCII transcriptions into numerical arrays for model training.
