OCR-D / core

        best_image = None

        alternative_images = segment.get_AlternativeImage()

        if alternative_images:

            # (e.g. from segment-level cropping, binarization, deskewing or despeckling)

            best_features = set()

            auto_features = {'cropped', 'deskewed', 'rotated-90', 'rotated-180', 'rotated-270'}

            # search to the end, because by convention we always append,

            # and among multiple satisfactory images we want the most recent,

            # but also ensure that we get the richest feature set, i.e. most

            # of those features that we cannot reproduce automatically below

            for alternative_image in alternative_images:

                if filename and filename != alternative_image.filename:

                    continue

                features = alternative_image.get_comments()

                if not features:

                    log.warning("AlternativeImage %d for segment '%s' does not have any feature attributes",

                                alternative_images.index(alternative_image) + 1, segment.id)

                    features = ''

                featureset = set(features.split(','))

                if (all(feature in featureset

                        for feature in feature_selector.split(',') if feature) and

                    not any(feature in featureset

                            for feature in feature_filter.split(',') if feature) and

                    len(featureset.difference(auto_features)) >= \

                    len(best_features.difference(auto_features))):

                    best_features = featureset

                    best_image = alternative_image

            if best_image:

                log.debug("Using AlternativeImage %d %s for segment '%s'",

                          alternative_images.index(best_image) + 1,

                          best_features, segment.id)

                segment_image = self._resolve_image_as_pil(alternative_image.get_filename())

                segment_coords['features'] = best_image.get_comments() # including duplicates

        alternative_image_features = segment_coords['features'].split(',')

        for duplicate_feature in set([feature for feature in alternative_image_features

        page_coords['features'] = ''

        best_image = None

        alternative_images = page.get_AlternativeImage()

        if alternative_images:

            # (e.g. from page-level cropping, binarization, deskewing or despeckling)

            best_features = set()

            auto_features = {'cropped', 'deskewed', 'rotated-90', 'rotated-180', 'rotated-270'}

            # search to the end, because by convention we always append,

            # and among multiple satisfactory images we want the most recent,

            # but also ensure that we get the richest feature set, i.e. most

            # of those features that we cannot reproduce automatically below

            for alternative_image in alternative_images:

                if filename and filename != alternative_image.filename:

                    continue

                features = alternative_image.get_comments()

                if not features:

                    log.warning("AlternativeImage %d for page '%s' does not have any feature attributes",

                                alternative_images.index(alternative_image) + 1, page_id)

                    features = ''

                featureset = set(features.split(','))

                if (all(feature in featureset

                        for feature in feature_selector.split(',') if feature) and

                    not any(feature in featureset

                            for feature in feature_filter.split(',') if feature) and

                    len(featureset.difference(auto_features)) >= \

                    len(best_features.difference(auto_features))):

                    best_features = featureset

                    best_image = alternative_image

            if best_image:

                log.debug("Using AlternativeImage %d %s for page '%s'",

                          alternative_images.index(best_image) + 1,

                          best_features, page_id)

                page_image = self._resolve_image_as_pil(best_image.get_filename())

                page_coords['features'] = best_image.get_comments() # including duplicates

        # adjust the coord transformation to the steps applied on the image,

        # and apply steps on the existing image in case it is missing there,