I have dataset consist of inkml files of handwritten texts. I want to convert it to a usable image format to train a CNN. python script would be helpful.
I found a method given below is the source code
def get_traces_data(inkml_file_abs_path):
traces_data = []
tree = ET.parse(inkml_file_abs_path)
root = tree.getroot()
doc_namespace = "{http://www.w3.org/2003/InkML}"
'Stores traces_all with their corresponding id'
traces_all = [{'id': trace_tag.get('id'),
'coords': [[round(float(axis_coord)) if float(axis_coord).is_integer() else round(float(axis_coord)) \
for axis_coord in coord[1:].split(' ')] if coord.startswith(' ') \
else [round(float(axis_coord)) if float(axis_coord).is_integer() else round(float(axis_coord)) \
for axis_coord in coord.split(' ')] \
for coord in (trace_tag.text).replace('\n', '').split(',')]} \
for trace_tag in root.findall(doc_namespace + 'trace')]
# print("before sort ", traces_all)
'Sort traces_all list by id to make searching for references faster'
traces_all.sort(key=lambda trace_dict: int(trace_dict['id']))
# print("after sort ", traces_all)
'Always 1st traceGroup is a redundant wrapper'
traceGroupWrapper = root.find(doc_namespace + 'traceGroup')
if traceGroupWrapper is not None:
for traceGroup in traceGroupWrapper.findall(doc_namespace + 'traceGroup'):
label = traceGroup.find(doc_namespace + 'annotation').text
'traces of the current traceGroup'
traces_curr = []
for traceView in traceGroup.findall(doc_namespace + 'traceView'):
'Id reference to specific trace tag corresponding to currently considered label'
traceDataRef = int(traceView.get('traceDataRef'))
'Each trace is represented by a list of coordinates to connect'
single_trace = traces_all[traceDataRef]['coords']
traces_curr.append(single_trace)
traces_data.append({'label': label, 'trace_group': traces_curr})
else:
'Consider Validation data that has no labels'
[traces_data.append({'trace_group': [trace['coords']]}) for trace in traces_all]
return traces_data
