This paper presents a method for generating complex sequences with long-range structure using Long Short-Term Memory (LSTM) recurrent neural networks (RNNs). The approach is demonstrated for text and online handwriting, and extended to handwriting synthesis by conditioning the network on a text sequence. The resulting system can generate highly realistic cursive handwriting in various styles. RNNs are dynamic models that can generate sequences by processing data one step at a time and predicting the next input. They are 'fuzzy' in that they do not use exact templates but instead use internal representations to interpolate between training examples. This allows RNNs to synthesize and reconstitute training data in a complex way, unlike template-based algorithms. However, standard RNNs struggle with long-term memory, leading to instability in sequence generation. LSTM, an RNN architecture, is better at storing and accessing information, making it suitable for sequence generation tasks. The paper describes a deep RNN composed of stacked LSTM layers, trained for next-step prediction and sequence generation. It applies this to text data from the Penn Treebank and Hutter Prize Wikipedia datasets, achieving performance competitive with state-of-the-art language models. The network can generate realistic text with long-range dependencies. For online handwriting, the paper uses a mixture density output layer to model real-valued data. It demonstrates the network's ability to learn letters and short words from pen traces and model global features of handwriting style. The network is also extended to generate handwriting synthesis by conditioning on a text sequence, allowing it to generate cursive handwriting samples that are indistinguishable from real data. The paper also discusses the use of adaptive weight noise to improve network performance and the importance of long-term memory in sequence generation. The results show that LSTM is better at adapting to new data than ordinary RNNs. The network's ability to generate realistic text and handwriting highlights its effectiveness in modeling complex sequences with long-range structure.This paper presents a method for generating complex sequences with long-range structure using Long Short-Term Memory (LSTM) recurrent neural networks (RNNs). The approach is demonstrated for text and online handwriting, and extended to handwriting synthesis by conditioning the network on a text sequence. The resulting system can generate highly realistic cursive handwriting in various styles. RNNs are dynamic models that can generate sequences by processing data one step at a time and predicting the next input. They are 'fuzzy' in that they do not use exact templates but instead use internal representations to interpolate between training examples. This allows RNNs to synthesize and reconstitute training data in a complex way, unlike template-based algorithms. However, standard RNNs struggle with long-term memory, leading to instability in sequence generation. LSTM, an RNN architecture, is better at storing and accessing information, making it suitable for sequence generation tasks. The paper describes a deep RNN composed of stacked LSTM layers, trained for next-step prediction and sequence generation. It applies this to text data from the Penn Treebank and Hutter Prize Wikipedia datasets, achieving performance competitive with state-of-the-art language models. The network can generate realistic text with long-range dependencies. For online handwriting, the paper uses a mixture density output layer to model real-valued data. It demonstrates the network's ability to learn letters and short words from pen traces and model global features of handwriting style. The network is also extended to generate handwriting synthesis by conditioning on a text sequence, allowing it to generate cursive handwriting samples that are indistinguishable from real data. The paper also discusses the use of adaptive weight noise to improve network performance and the importance of long-term memory in sequence generation. The results show that LSTM is better at adapting to new data than ordinary RNNs. The network's ability to generate realistic text and handwriting highlights its effectiveness in modeling complex sequences with long-range structure.