Paper Title
TYPOPDF: Typed-To-Handwritten Document Generation Using RNN- LSTM Based Sequence Modeling

Abstract
Handwriting generation is a challenging sequence modeling problem in which a neural network must learn both writing dynamics and character alignment over time. In this work, we present TypoPDF, a practical handwritingsynthesis system built using a deep recurrent neural network architecture. The proposed model consists of stacked Long Short-Term Memory (LSTM) layers in conjunction with a soft attention mechanism, allowing the network to selectively attend to pertinent characters during the generation of pen stroke sequences. Handwriting generation is a challenging sequencemodeling task because a system must learn the relationship between written characters and continuous pen movements. In this work, we present TypoPDF, a handwriting synthesis system developed using a deep recurrent neural network architecture. The model is built using stacked Long Short-Term Memory (LSTM) layers together with a soft attention mechanism, which allows the network to focus on relevant characters while generating pen stroke sequences. Unlike conventional image-based handwriting generators, this approach works directly with pen trajectory data represented as (Δx, Δy, end-of-stroke) sequences. To capture the natural variations present in human writing, a Mixture Density Network (MDN) is used in the output layer. Instead of predicting a single coordinate, the network estimates parameters of multiple Gaussian distributions, enabling the generated handwriting to appear smoother and more natural. The model is trained on sequential handwriting data and generates strokes in an autoregressive manner during inference. In addition to the core model, a web-based framework has been developed to convert generated stroke sequences into Scalable Vector Graphics (SVG). The system also supports background overlays and exports the final output as a single-page PDF document. Experimental observations indicate that the generated handwriting is coherent and visually realistic while maintaining alignment with the input text. This work demonstrates how deep sequential models can be combined with document rendering techniques to build practical handwriting generation systems. The system is trained on sequential handwriting data and performs autoregressive stroke generation during inference. In addition to the core model, we develop a complete web-based framework that converts generated stroke sequences into scalable vector graphics (SVG), applies optional background overlays, and exports the final output as a Single-page PDF document. The entire pipeline operates dynamically, enabling real-time preview and customization of writing style, ink color, and layout. Experimental observations show that the model is capable of producing coherent and visually realistic handwriting that maintains alignment with the provided input text. The proposed system demonstrates how deep sequential models can be effectively integrated into practical document generation applications. Keywords - Handwriting Synthesis, LSTM, Attention Mechanism, Mixture Density Network, Stroke Generation, Document Automation.