How to create a Python tool for optical music recognition and digital score creation?

Creating a Python tool for Optical Music Recognition (OMR) and Digital Score Creation is a complex project that we can break down into several steps. Here are high-level directions:

Step 1: Setting up the project

To start, you must have the Python development environment installed (Python 3 and pip). You'll need libraries such as Opencv (for image processing), PIL or Pillow (for image manipulation), numpy (for mathematical operations), pytesseract (for OCR), music21 (for manipulation of musical scores) and so on.

Install these packages using pip:

pip install opencv-python
pip install pillow
pip install numpy
pip install pytesseract
pip install music21

Step 2: Preprocessing the image

We start with image preprocessing to remove any noise and to simplify the image. We will convert the image to a binary.

import cv2

def preprocess_image(image_path):
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)[1]
    return binary

Step 3: Detecting and processing the staff lines

Next, you'll need to detect the staff lines from the musical score.

def process_staff_lines(binary_img):
    # Perform morphological operations
    d = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
    dilated = cv2.dilate(binary_img, d)

    # Find contours
    contours, _ = cv2.findContours(255 - dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[-2:]

    # Filter contours
    filtered = filter_contours(contours)

    return filtered

Step 4: Symbol segmentation

Now, we would segment the notes. Try to segment the noise-free image into its individual symbols such as clefs, noteheads, stems and so on.

def segment_notes(filtered_img):
    # Apply morphological operations
    d = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
    dilated = cv2.dilate(filtered_img, d)

    # Find contours
    contours, _ = cv2.findContours(255 - dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[-2:]

    return contours

Step 5: Symbol recognition

Once you've segmented the images into its individual symbols, you can now create a function to recognize these symbols. You can use libraries like pytesseract for this purpose.

Step 6: Conversion to a music notation format

The final step is to convert the recognized symbols into a music notation software format such as MusicXML using the music21 library.

from music21 import *
def write_to_musicxml(notes, filename):
    stream1 = stream.Stream()
    for note in notes:
        stream1.append(note.Note())
    stream1.write('musicxml', fp=filename)

Remember, you may need to train your model beforehand using a set of musical score images. This is due to symbol recognition, especially with Music, being a complex process and a pre-trained model might be necessary for accurate results.

The sample implementations above are simplistic and non-effective, they've been simplified greatly due to the complexities of each step in the whole process. You will have to dig deep and understand how to process musical scores, understand the different types of notes, and so on. This is definitely not a beginner-friendly project, but studying about Image Processing and machine learning will surely help you set things up here.