def compute_error(model: TorchSeqRecognizer, validation_set: Sequence[Tuple[str, str]]) -> Tuple[int, int]:
"""
Computes error report from a model and a list of line image-text pairs.
Args:
model (kraken.lib.models.TorchSeqRecognizer): Model used for recognition
validation_set (list): List of tuples (image, text) for validation
Returns:
A tuple with total number of characters and edit distance across the
whole validation set.
"""
total_chars = 0
error = 0
for im, text in validation_set:
pred = model.predict_string(im)
total_chars += len(text)
error += _fast_levenshtein(pred, text)
return total_chars, error
def compute_error(model: TorchSeqRecognizer, validation_set: Iterable[Dict[str, torch.Tensor]]) -> Tuple[int, int]:
"""
Computes error report from a model and a list of line image-text pairs.
Args:
model (kraken.lib.models.TorchSeqRecognizer): Model used for recognition
validation_set (list): List of tuples (image, text) for validation
Returns:
A tuple with total number of characters and edit distance across the
whole validation set.
"""
total_chars = 0
error = 0
for batch in validation_set:
preds = model.predict_string(batch['image'], batch['seq_lens'])
total_chars += batch['target_lens'].sum()
for pred, text in zip(preds, batch['target']):
error += _fast_levenshtein(pred, text)
return total_chars, error
def rpred(network: TorchSeqRecognizer,
im: Image.Image,
bounds: dict,
pad: int = 16,
bidi_reordering: bool = True) -> Generator[ocr_record, None, None]:
"""
Uses a RNN to recognize text
Args:
network (kraken.lib.models.TorchSeqRecognizer): A TorchSegRecognizer
object
im (PIL.Image.Image): Image to extract text from
bounds (dict): A dictionary containing a 'boxes' entry with a list of
coordinates (x0, y0, x1, y1) of a text line in the image
and an entry 'text_direction' containing
'horizontal-lr/rl/vertical-lr/rl'.
pad (int): Extra blank padding to the left and right of text line.
Auto-disabled when expected network inputs are incompatible
with padding.
bidi_reordering (bool): Reorder classes in the ocr_record according to
the Unicode bidirectional algorithm for correct
display.
Yields:
An ocr_record containing the recognized text, absolute character
positions, and confidence values for each character.
"""
im_str = get_im_str(im)
logger.info('Running recognizer on {} with {} lines'.format(
im_str, len(bounds['boxes'])))
logger.debug('Loading line transform')
batch, channels, height, width = network.nn.input
ts = generate_input_transforms(batch, height, width, channels, pad)
for box, coords in extract_boxes(im, bounds):
# check if boxes are non-zero in any dimension
if sum(coords[::2]) == 0 or coords[3] - coords[1] == 0:
logger.warning(
'bbox {} with zero dimension. Emitting empty record.'.format(
coords))
yield ocr_record('', [], [])
continue
# try conversion into tensor
try:
line = ts(box)
except Exception:
yield ocr_record('', [], [])
continue
# check if line is non-zero
if line.max() == line.min():
yield ocr_record('', [], [])
continue
preds = network.predict(line)
# calculate recognized LSTM locations of characters
# scale between network output and network input
net_scale = line.shape[2] / network.outputs.shape[1]
# scale between network input and original line
in_scale = box.size[0] / (line.shape[2] - 2 * pad)
def _scale_val(val, min_val, max_val):
return int(
round(
min(max(((val * net_scale) - pad) * in_scale, min_val),
max_val)))
# XXX: fix bounding box calculation ocr_record for multi-codepoint labels.
pred = ''.join(x[0] for x in preds)
pos = []
conf = []
for _, start, end, c in preds:
if bounds['text_direction'].startswith('horizontal'):
xmin = coords[0] + _scale_val(start, 0, box.size[0])
xmax = coords[0] + _scale_val(end, 0, box.size[0])
pos.append((xmin, coords[1], xmax, coords[3]))
else:
ymin = coords[1] + _scale_val(start, 0, box.size[1])
ymax = coords[1] + _scale_val(start, 0, box.size[1])
pos.append((coords[0], ymin, coords[2], ymax))
conf.append(c)
if bidi_reordering:
logger.debug('BiDi reordering record.')
yield bidi_record(ocr_record(pred, pos, conf))
else:
logger.debug('Emitting raw record')
yield ocr_record(pred, pos, conf)
def rpred(network: TorchSeqRecognizer,
im: Image.Image,
bounds: dict,
pad: int = 16,
bidi_reordering: bool = True) -> Generator[ocr_record, None, None]:
"""
Uses a RNN to recognize text
Args:
network (kraken.lib.models.TorchSeqRecognizer): A TorchSegRecognizer
object
im (PIL.Image.Image): Image to extract text from
bounds (dict): A dictionary containing a 'boxes' entry with a list of
coordinates (x0, y0, x1, y1) of a text line in the image
and an entry 'text_direction' containing
'horizontal-lr/rl/vertical-lr/rl'.
pad (int): Extra blank padding to the left and right of text line.
Auto-disabled when expected network inputs are incompatible
with padding.
bidi_reordering (bool): Reorder classes in the ocr_record according to
the Unicode bidirectional algorithm for correct
display.
Yields:
An ocr_record containing the recognized text, absolute character
positions, and confidence values for each character.
"""
im_str = get_im_str(im)
logger.info('Running recognizer on {} with {} lines'.format(im_str, len(bounds['boxes'])))
logger.debug('Loading line transform')
batch, channels, height, width = network.nn.input
ts = generate_input_transforms(batch, height, width, channels, pad)
for box, coords in extract_boxes(im, bounds):
# check if boxes are non-zero in any dimension
if sum(coords[::2]) == 0 or coords[3] - coords[1] == 0:
logger.warning('bbox {} with zero dimension. Emitting empty record.'.format(coords))
yield ocr_record('', [], [])
continue
# try conversion into tensor
try:
line = ts(box)
except Exception:
yield ocr_record('', [], [])
continue
# check if line is non-zero
if line.max() == line.min():
yield ocr_record('', [], [])
continue
preds = network.predict(line)
# calculate recognized LSTM locations of characters
# scale between network output and network input
net_scale = line.shape[2]/network.outputs.shape[1]
# scale between network input and original line
in_scale = box.size[0]/(line.shape[2]-2*pad)
def _scale_val(val, min_val, max_val):
return int(round(min(max(((val*net_scale)-pad)*in_scale, min_val), max_val)))
# XXX: fix bounding box calculation ocr_record for multi-codepoint labels.
pred = ''.join(x[0] for x in preds)
pos = []
conf = []
for _, start, end, c in preds:
if bounds['text_direction'].startswith('horizontal'):
xmin = coords[0] + _scale_val(start, 0, box.size[0])
xmax = coords[0] + _scale_val(end, 0, box.size[0])
pos.append((xmin, coords[1], xmax, coords[3]))
else:
ymin = coords[1] + _scale_val(start, 0, box.size[1])
ymax = coords[1] + _scale_val(start, 0, box.size[1])
pos.append((coords[0], ymin, coords[2], ymax))
conf.append(c)
if bidi_reordering:
logger.debug('BiDi reordering record.')
yield bidi_record(ocr_record(pred, pos, conf))
else:
logger.debug('Emitting raw record')
yield ocr_record(pred, pos, conf)