Symbols Contexts

Starting with the annotated score images, we extracted the so-called "full-context" sub-image for each symbol.

As input, we had a score image properly scaled and a list of symbol information (shape name and bounding box within the score image).

For an interline value normalized at 10 pixels, we used a fixed rectangular window (width: 48 pixels, height: 96 pixels) centered on each symbol center.

Note: These numbers were chosen rather arbitrarily and could be modified is so desired. However, the context dimensions must be numbers multiple of 4, to accommodate the two sub-sampling layers used by the current convolutional neural network.

Via the program Features, we produced a .CSV file to be later used for the training of the CNN. In this file and for each symbol, we simply added a row that contained:

• The pixel values of the context window, row by row
• The index of the symbol name in the ordered list of symbol names

To check this material, we wrote a simple program (Subimages) that read this .csv file to generate the corresponding symbol sub-images that could be easily checked by visual inspection.