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armrest

Armrest is a support library for building applications for the reMarkable tablet.

It currently consists of two main parts:

• A Python machine learning pipeline, used to train a TensorFlow Lite handwriting recognition model.
• A Rust library for high-level application development, built on libremarkable, and including:
  • A handwriting recognition module, with support for custom language models.
  • An Elm-inspired UI library.

Building

The rust library is build using cargo. You'll need to set up the build as described in libremarkable.

The build.rm wrapper in the project root builds for the tablet. You'll need the remarkable toolchain downloaded and unpacked somewhere; set RM_TOOLCHAIN=<toolchain path> to point the build script to it.

Building the tflite dependency takes a long time... often several minutes on a reasonably powerful machine. Sorry about that!

Handwriting recognition

The handwriting recognizer uses a deep LSTM-based architecture, inspired chiefly by the following papers:

• Fast Multi-language LSTM-based Online Handwriting Recognition
• A novel connectionist system for unconstrained handwriting recognition

The full training pipeline is implemented in Python... except for the input normalization and encoding, which is implemented in Rust so it can share code with the runtime HWR. As a result we need some standard data formats to share the data between Rust and Python.

Formats

Handwriting data is available in all sorts of formats, many of which are annoying to parse. armrest uses a few simple text-based formats for ease of implementation in multiple languages.

In all cases, records separated by newline characters. Records are also often accompanied by the text string they correspond to; in that case, each line includes the string, a tab character, and then the raw data. (Text strings should not contain tabs... or any whitespace besides the ASCII space.)

Inks

Points are made up of three space-separated decimal values - two for the x / y position and one for time. The y coordinate grows downward. Points are separated by commas, and strokes (sequences of connected points) are separated by semicolons. A set of strokes that makes up a single logical input is called an ink.

Tensors

In this context, tensor is a two-dimensional matrix: a variable-size series of fixed-size steps. The (decimal) values within a step are separated by spaces; steps are separated by commas. Types of tensors include:

• spline - Each step consists of 4 values: one each for x, y, and t, and a fourth which is 1 iff the point is the last in a stroke, and 0 otherwise. x, y, and t are 0 for the first point in an ink, and relative to the previous point in the ink for all other points.
• bezier - A bezier-curve-based encoding. This is currently experimental, unspecified, and unused.