This is a fine grained document pixel labelling tool for layout analysis purposes. It uses a FCN-style deep network with conditional random field postprocessing to assign each pixel of an input image to a particular class (background, main text, decoration, annotation).

Everything is highly experimental, subject to changes without notice, and will break frequently.

Run:

::

$ pip3 install .

to install the dependencies and the command line tool. For development purposes use:

::

$ pip3 install --editable .

Training requires a directory with input images in JPG and their corresponding labelled ground truth in PNG format. The labels should correspond to the hisDB standard, i.e. 1-bit per class in the lowest 4bits of the red color channel.

There are half a dozen options that don't really improve training results, notably per-class loss weights, encoder refinement, and augmentation.

Run:

::

$ seg pred -m $model_file $img_1 $img_2 ... $img_n

Outputs are the original file name plus a class_n suffix and an opaque overlay image per input file.