sjvasquez/handwriting-synthesis
Handwriting Synthesis
This project is a neural script generator that uses a recurrent neural network to synthesize human-like handwriting. It maps ASCII text characters to realistic pen stroke coordinates through an attention mechanism to mimic natural writing patterns.
The system allows for handwriting style customization by adjusting priming and biasing parameters to control the neatness and stylistic characteristics of the generated text. Users can also define output formatting, including stroke colors and line widths, for the resulting digital scripts.
The project includes a full neural network training workflow, featuring a data pipeline that normalizes XML stroke data and ASCII transcriptions into numerical arrays. Training infrastructure covers gradient clipping, validation-based checkpointing, and metrics logging to monitor accuracy and loss.
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.
Star history
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