API Reference

kraken.blla module

Note

blla provides the interface to the fully trainable segmenter. For the legacy segmenter interface refer to the pageseg module. Note that recognition models are not interchangeable between segmenters.

kraken.blla.segment(im, text_direction='horizontal-lr', mask=None, reading_order_fn=polygonal_reading_order, model=None, device='cpu')

Segments a page into text lines using the baseline segmenter.

Segments a page into text lines and returns the polyline formed by each baseline and their estimated environment.

Parameters:

• im (PIL.Image.Image) – Input image. The mode can generally be anything but it is possible to supply a binarized-input-only model which requires accordingly treated images.

• text_direction (str) – Passed-through value for serialization.serialize.

• mask (Optional[numpy.ndarray]) – A bi-level mask image of the same size as im where 0-valued regions are ignored for segmentation purposes. Disables column detection.

• reading_order_fn (Callable) – Function to determine the reading order. Has to accept a list of tuples (baselines, polygon) and a text direction (lr or rl).

• model (Union[List[kraken.lib.vgsl.TorchVGSLModel], kraken.lib.vgsl.TorchVGSLModel]) – One or more TorchVGSLModel containing a segmentation model. If none is given a default model will be loaded.

• device (str) – The target device to run the neural network on.

Returns:

A dictionary containing the text direction and under the key ‘lines’ a list of reading order sorted baselines (polylines) and their respective polygonal boundaries. The last and first point of each boundary polygon are connected.

 {'text_direction': '$dir',
  'type': 'baseline',
  'lines': [
     {'baseline': [[x0, y0], [x1, y1], ..., [x_n, y_n]], 'boundary': [[x0, y0, x1, y1], ... [x_m, y_m]]},
     {'baseline': [[x0, ...]], 'boundary': [[x0, ...]]}
   ]
   'regions': [
     {'region': [[x0, y0], [x1, y1], ..., [x_n, y_n]], 'type': 'image'},
     {'region': [[x0, ...]], 'type': 'text'}
   ]
 }

Raises:

• KrakenInvalidModelException – if the given model is not a valid segmentation model.

• KrakenInputException – if the mask is not bitonal or does not match the image size.

Return type:

Dict[str, Any]

kraken.pageseg module

Note

pageseg is the legacy bounding box-based segmenter. For the trainable baseline segmenter interface refer to the blla module. Note that recognition models are not interchangeable between segmenters.

kraken.pageseg.segment(im, text_direction='horizontal-lr', scale=None, maxcolseps=2, black_colseps=False, no_hlines=True, pad=0, mask=None, reading_order_fn=reading_order)

Segments a page into text lines.

Segments a page into text lines and returns the absolute coordinates of each line in reading order.

Parameters:

• im – A bi-level page of mode ‘1’ or ‘L’

• text_direction (str) – Principal direction of the text (horizontal-lr/rl/vertical-lr/rl)

• scale (Optional[float]) – Scale of the image. Will be auto-determined if set to None.

• maxcolseps (float) – Maximum number of whitespace column separators

• black_colseps (bool) – Whether column separators are assumed to be vertical black lines or not

• no_hlines (bool) – Switch for small horizontal line removal.

• pad (Union[int, Tuple[int, int]]) – Padding to add to line bounding boxes. If int the same padding is used both left and right. If a 2-tuple, uses (padding_left, padding_right).

• mask (Optional[numpy.ndarray]) – A bi-level mask image of the same size as im where 0-valued regions are ignored for segmentation purposes. Disables column detection.

• reading_order_fn (Callable) – Function to call to order line output. Callable accepting a list of slices (y, x) and a text direction in (rl, lr).

Returns:

A dictionary containing the text direction and a list of reading order sorted bounding boxes under the key ‘boxes’:

{'text_direction': '$dir', 'boxes': [(x1, y1, x2, y2),...]}

Raises:

KrakenInputException – if the input image is not binarized or the text direction is invalid.

Return type:

Dict[str, Any]

kraken.rpred module

kraken.rpred.bidi_record(record, base_dir=None)

Reorders a record using the Unicode BiDi algorithm.

Models trained for RTL or mixed scripts still emit classes in LTR order requiring reordering for proper display.

Parameters:

record (kraken.rpred.ocr_record) –

Returns:

kraken.rpred.ocr_record

Return type:

ocr_record

class kraken.rpred.mm_rpred(nets, im, bounds, pad=16, bidi_reordering=True, tags_ignore=None)

Multi-model version of kraken.rpred.rpred

Parameters:

• nets (Dict[str, kraken.lib.models.TorchSeqRecognizer]) –

• im (PIL.Image.Image) –

• bounds (dict) –

• pad (int) –

• bidi_reordering (Union[bool, str]) –

• tags_ignore (Optional[List[str]]) –

class kraken.rpred.ocr_record(prediction, cuts, confidences, line)

A record object containing the recognition result of a single line

Parameters:

• prediction (str) –

• confidences (List[float]) –

• line (Union[List, Dict[str, List]]) –

kraken.rpred.rpred(network, im, bounds, pad=16, bidi_reordering=True)

Uses a TorchSeqRecognizer and a segmentation to recognize text

Parameters:

• 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|str) – Reorder classes in the ocr_record according to the Unicode bidirectional algorithm for correct display. Set to L|R to change base text direction.

Yields:

An ocr_record containing the recognized text, absolute character positions, and confidence values for each character.

Return type:

Generator[ocr_record, None, None]

kraken.serialization module

kraken.serialization.render_report(model, chars, errors, char_confusions, scripts, insertions, deletions, substitutions)

Renders an accuracy report.

Parameters:

• model (str) – Model name.

• errors (int) – Number of errors on test set.

• char_confusions (dict) – Dictionary mapping a tuple (gt, pred) to a number of occurrences.

• scripts (dict) – Dictionary counting character per script.

• insertions (dict) – Dictionary counting insertion operations per Unicode script

• deletions (int) – Number of deletions

• substitutions (dict) – Dictionary counting substitution operations per Unicode script.

• chars (int) –

Returns:

A string containing the rendered report.

Return type:

str

kraken.serialization.serialize(records, image_name=None, image_size=(0, 0), writing_mode='horizontal-tb', scripts=None, regions=None, template='hocr')

Serializes a list of ocr_records into an output document.

Serializes a list of predictions and their corresponding positions by doing some hOCR-specific preprocessing and then renders them through one of several jinja2 templates.

Note: Empty records are ignored for serialization purposes.

Parameters:

• records (iterable) – List of kraken.rpred.ocr_record

• image_name (str) – Name of the source image

• image_size (tuple) – Dimensions of the source image

• writing_mode (str) – Sets the principal layout of lines and the direction in which blocks progress. Valid values are horizontal-tb, vertical-rl, and vertical-lr.

• scripts (list) – List of scripts contained in the OCR records

• regions (list) – Dictionary mapping region types to a list of region polygons.

• template (str) – Selector for the serialization format. May be ‘hocr’ or ‘alto’.

Returns:

(str) rendered template.

Return type:

str

kraken.serialization.serialize_segmentation(segresult, image_name=None, image_size=(0, 0), template='hocr')

Serializes a segmentation result into an output document.

Parameters:

• segresult (Dict[str, Any]) – Result of blla.segment

• image_name (str) – Name of the source image

• image_size (tuple) – Dimensions of the source image

• template (str) – Selector for the serialization format. May be ‘hocr’ or ‘alto’.

Returns:

(str) rendered template.

Return type:

str

kraken.lib.models module

class kraken.lib.models.TorchSeqRecognizer(nn, decoder=kraken.lib.ctc_decoder.greedy_decoder, train=False, device='cpu')

A wrapper class around a TorchVGSLModel for text recognition.

Parameters:

• nn (kraken.lib.vgsl.TorchVGSLModel) –

• train (bool) –

• device (str) –

forward(line, lens=None)

Performs a forward pass on a torch tensor of one or more lines with shape (N, C, H, W) and returns a numpy array (N, W, C).

Parameters:

• line (torch.Tensor) – NCHW line tensor

• lens (torch.Tensor) – Optional tensor containing sequence lengths if N > 1

Returns:

Tuple with (N, W, C) shaped numpy array and final output sequence lengths.

Raises:

KrakenInputException – Is raised if the channel dimension isn’t of size 1 in the network output.

Return type:

Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray]]

predict(line, lens=None)

Performs a forward pass on a torch tensor of a line with shape (N, C, H, W) and returns the decoding as a list of tuples (string, start, end, confidence).

Parameters:

• line (torch.Tensor) – NCHW line tensor

• lens (Optional[torch.Tensor]) – Optional tensor containing sequence lengths if N > 1

Returns:

List of decoded sequences.

Return type:

List[List[Tuple[str, int, int, float]]]

predict_labels(line, lens=None)

Performs a forward pass on a torch tensor of a line with shape (N, C, H, W) and returns a list of tuples (class, start, end, max). Max is the maximum value of the softmax layer in the region.

Parameters:

• line (torch.tensor) –

• lens (torch.Tensor) –

Return type:

List[List[Tuple[int, int, int, float]]]

predict_string(line, lens=None)

Performs a forward pass on a torch tensor of a line with shape (N, C, H, W) and returns a string of the results.

Parameters:

• line (torch.Tensor) – NCHW line tensor

• lens (Optional[torch.Tensor]) – Optional tensor

Return type:

List[str]

to(device)

Moves model to device and automatically loads input tensors onto it.

kraken.lib.models.load_any(fname, train=False, device='cpu')

Loads anything that was, is, and will be a valid ocropus model and instantiates a shiny new kraken.lib.lstm.SeqRecognizer from the RNN configuration in the file.

Currently it recognizes the following kinds of models:

• protobuf models containing converted python BIDILSTMs (recognition only)

• protobuf models containing CLSTM networks (recognition only)

• protobuf models containing VGSL segmentation and recognitino networks.

Additionally an attribute ‘kind’ will be added to the SeqRecognizer containing a string representation of the source kind. Current known values are:

• pyrnn for pickled BIDILSTMs

• clstm for protobuf models generated by clstm

• vgsl for VGSL models

Parameters:

• fname (str) – Path to the model

• train (bool) – Enables gradient calculation and dropout layers in model.

• device (str) – Target device

Returns:

A kraken.lib.models.TorchSeqRecognizer object.

Raises:

KrakenInvalidModelException – if the model is not loadable by any parser.

Return type:

TorchSeqRecognizer

kraken.lib.vgsl module

class kraken.lib.vgsl.TorchVGSLModel(spec)

Class building a torch module from a VSGL spec.

The initialized class will contain a variable number of layers and a loss function. Inputs and outputs are always 4D tensors in order (batch, channels, height, width) with channels always being the feature dimension.

Importantly this means that a recurrent network will be fed the channel vector at each step along its time axis, i.e. either put the non-time-axis dimension into the channels dimension or use a summarizing RNN squashing the time axis to 1 and putting the output into the channels dimension respectively.

Parameters:

spec (str) –

input

Expected input tensor as a 4-tuple.

Type:

tuple

nn

Stack of layers parsed from the spec.

Type:

torch.nn.Sequential

criterion

Fully parametrized loss function.

Type:

torch.nn.Module

user_metadata

dict with user defined metadata. Is flushed into model file during saving/overwritten by loading operations.

Type:

dict

one_channel_mode

Field indicating the image type used during training of one-channel images. Is ‘1’ for models trained on binarized images, ‘L’ for grayscale, and None otherwise.

Type:

str

add_codec(codec)

Adds a PytorchCodec to the model.

Parameters:

codec (kraken.lib.codec.PytorchCodec) –

Return type:

None

append(idx, spec)

Splits a model at layer idx and append layers spec.

New layers are initialized using the init_weights method.

Parameters:

• idx (int) – Index of layer to append spec to starting with 1. To select the whole layer stack set idx to None.

• spec (str) – VGSL spec without input block to append to model.

Return type:

None

build_addition(input, blocks, idx)

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_conv(input, blocks, idx)

Builds a 2D convolution layer.

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_dropout(input, blocks, idx)

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_groupnorm(input, blocks, idx)

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_identity(input, blocks, idx)

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_maxpool(input, blocks, idx)

Builds a maxpool layer.

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_output(input, blocks, idx)

Builds an output layer.

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_parallel(input, blocks, idx)

Builds a block of parallel layers.

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_reshape(input, blocks, idx)

Builds a reshape layer

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_rnn(input, blocks, idx)

Builds an LSTM/GRU layer returning number of outputs and layer.

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

build_series(input, blocks, idx)

Builds a serial block of layers.

Parameters:

• input (Tuple[int, int, int, int]) –

• blocks (List[str]) –

• idx (int) –

Return type:

Union[Tuple[None, None, None], Tuple[Tuple[int, int, int, int], str, Callable]]

eval()

Sets the model to evaluation/inference mode, disabling dropout and gradient calculation.

Return type:

None

property hyper_params

init_weights(idx=slice(0, None))

Initializes weights for all or a subset of layers in the graph.

LSTM/GRU layers are orthogonally initialized, convolutional layers uniformly from (-0.1,0.1).

Parameters:

idx (slice) – A slice object representing the indices of layers to initialize.

Return type:

None

classmethod load_clstm_model(path)

Loads an CLSTM model to VGSL.

Parameters:

path (Union[str, pathlib.Path]) –

classmethod load_model(path)

Deserializes a VGSL model from a CoreML file.

Parameters:

path (Union[str, pathlib.Path]) – CoreML file

Returns:

A TorchVGSLModel instance.

Raises:

• KrakenInvalidModelException if the model data is invalid (not a –

• string, protobuf file, or without appropriate metadata). –

• FileNotFoundError if the path doesn't point to a file. –

classmethod load_pronn_model(path)

Loads an pronn model to VGSL.

Parameters:

path (Union[str, pathlib.Path]) –

property model_type

property one_channel_mode

resize_output(output_size, del_indices=None)

Resizes an output layer.

Parameters:

• output_size (int) – New size/output channels of last layer

• del_indices (list) – list of outputs to delete from layer

Return type:

None

save_model(path)

Serializes the model into path.

Parameters:

path (str) – Target destination

property seg_type

set_num_threads(num)

Sets number of OpenMP threads to use.

Parameters:

num (int) –

Return type:

None

to(device)

Parameters:

device (Union[str, torch.device]) –

Return type:

None

train()

Sets the model to training mode (enables dropout layers and disables softmax on CTC layers).

Return type:

None

kraken.lib.xml module

kraken.lib.xml.parse_xml(filename)

Parses either a PageXML or ALTO file with autodetermination of the file format.

Parameters:

filename (Union[str, pathlib.Path]) – path to an XML file.

Returns:

A dict:

{'image': impath,
 'lines': [{'boundary': [[x0, y0], ...],
            'baseline': [[x0, y0], ...],
            'text': apdjfqpf',
            'tags': {'type': 'default', ...}},
           ...
           {...}],
 'regions': {'region_type_0': [[[x0, y0], ...], ...], ...}}

Return type:

Dict[str, Any]

kraken.lib.xml.parse_page(filename)

Parses a PageXML file, returns the baselines defined in it, and loads the referenced image.

Parameters:

filename (Union[str, pathlib.Path]) – path to a PageXML file.

Returns:

A dict:

{'image': impath,
 'lines': [{'boundary': [[x0, y0], ...],
            'baseline': [[x0, y0], ...],
            'text': apdjfqpf',
            'tags': {'type': 'default', ...}},
           ...
           {...}],
 'regions': {'region_type_0': [[[x0, y0], ...], ...], ...}}

Return type:

Dict[str, Any]

kraken.lib.xml.parse_alto(filename)

Parses an ALTO file, returns the baselines defined in it, and loads the referenced image.

Parameters:

filename (Union[str, pathlib.Path]) – path to an ALTO file.

Returns:

A dict:

{'image': impath,
 'lines': [{'boundary': [[x0, y0], ...],
            'baseline': [[x0, y0], ...],
            'text': apdjfqpf',
            'tags': {'type': 'default', ...}},
           ...
           {...}],
 'regions': {'region_type_0': [[[x0, y0], ...], ...], ...}}

Return type:

Dict[str, Any]

kraken.lib.codec module

class kraken.lib.codec.PytorchCodec(charset, strict=False)

Builds a codec converting between graphemes/code points and integer label sequences.

charset may either be a string, a list or a dict. In the first case each code point will be assigned a label, in the second case each string in the list will be assigned a label, and in the final case each key string will be mapped to the value sequence of integers. In the first two cases labels will be assigned automatically. When a mapping is manually provided the label codes need to be a prefix-free code.

As 0 is the blank label in a CTC output layer, output labels and input dictionaries are/should be 1-indexed.

Parameters:

• charset (Union[Dict[str, Sequence[int]], Sequence[str], str]) – Input character set.

• strict – Flag indicating if encoding/decoding errors should be ignored or cause an exception.

Raises:

KrakenCodecException – If the character set contains duplicate entries or the mapping is non-singular or non-prefix-free.

add_labels(charset)

Adds additional characters/labels to the codec.

charset may either be a string, a list or a dict. In the first case each code point will be assigned a label, in the second case each string in the list will be assigned a label, and in the final case each key string will be mapped to the value sequence of integers. In the first two cases labels will be assigned automatically.

As 0 is the blank label in a CTC output layer, output labels and input dictionaries are/should be 1-indexed.

Parameters:

charset (Union[Dict[str, Sequence[int]], Sequence[str], str]) – Input character set.

Return type:

PytorchCodec

decode(labels)

Decodes a labelling.

Given a labelling with cuts and confidences returns a string with the cuts and confidences aggregated across label-code point correspondences. When decoding multilabels to code points the resulting cuts are min/max, confidences are averaged.

Parameters:

labels (Sequence[Tuple[int, int, int, float]]) – Input containing tuples (label, start, end, confidence).

Returns:

A list of tuples (code point, start, end, confidence)

Return type:

List[Tuple[str, int, int, float]]

encode(s)

Encodes a string into a sequence of labels.

If the code is non-singular we greedily encode the longest sequence first.

Parameters:

s (str) – Input unicode string

Returns:

Ecoded label sequence

Raises:

KrakenEncodeException – if the a subsequence is not encodable and the codec is set to strict mode.

Return type:

torch.IntTensor

property is_valid

Returns True if the codec is prefix-free (in label space) and non-singular (in both directions).

Return type:

bool

property max_label

Returns the maximum label value.

Return type:

int

merge(codec)

Transforms this codec (c1) into another (c2) reusing as many labels as possible.

The resulting codec is able to encode the same code point sequences while not necessarily having the same labels for them as c2. Retains matching character -> label mappings from both codecs, removes mappings not c2, and adds mappings not in c1. Compound labels in c2 for code point sequences not in c1 containing labels also in use in c1 are added as separate labels.

Parameters:

codec (PytorchCodec) – PytorchCodec to merge with

Returns:

A merged codec and a list of labels that were removed from the original codec.

Return type:

Tuple[PytorchCodec, Set]

kraken.lib.train module

Training Schedulers

Training Stoppers

Loss and Evaluation Functions

Trainer

class kraken.lib.train.KrakenTrainer(callbacks=None, enable_progress_bar=True, enable_summary=True, min_epochs=5, max_epochs=100, *args, **kwargs)

Parameters:

• callbacks (Optional[Union[List[pytorch_lightning.callbacks.Callback], pytorch_lightning.callbacks.Callback]]) –

• enable_progress_bar (bool) –

• enable_summary (bool) –

fit(*args, **kwargs)

kraken.lib.dataset module

Datasets

class kraken.lib.dataset.BaselineSet(imgs=None, suffix='.path', line_width=4, im_transforms=transforms.Compose([]), mode='path', augmentation=False, valid_baselines=None, merge_baselines=None, valid_regions=None, merge_regions=None)

Dataset for training a baseline/region segmentation model.

Parameters:

• imgs (Sequence[str]) –

• suffix (str) –

• line_width (int) –

• im_transforms (Callable[[Any], torch.Tensor]) –

• mode (str) –

• augmentation (bool) –

• valid_baselines (Sequence[str]) –

• merge_baselines (Dict[str, Sequence[str]]) –

• valid_regions (Sequence[str]) –

• merge_regions (Dict[str, Sequence[str]]) –

add(image, baselines=None, regions=None, *args, **kwargs)

Adds a page to the dataset.

Parameters:

• im (path) – Path to the whole page image

• baseline (dict) – A list containing dicts with a list of coordinates and tags [{‘baseline’: [[x0, y0], …, [xn, yn]], ‘tags’: (‘script_type’,)}, …]

• regions (dict) – A dict containing list of lists of coordinates {‘region_type_0’: [[x0, y0], …, [xn, yn]]], ‘region_type_1’: …}.

• image (Union[str, PIL.Image.Image]) –

• baselines (List[List[List[Tuple[int, int]]]]) –

transform(image, target)

class kraken.lib.dataset.PolygonGTDataset(normalization=None, whitespace_normalization=True, reorder=True, im_transforms=transforms.Compose([]), augmentation=False)

Dataset for training a line recognition model from polygonal/baseline data.

Parameters:

• normalization (Optional[str]) –

• whitespace_normalization (bool) –

• reorder (Union[bool, str]) –

• im_transforms (Callable[[Any], torch.Tensor]) –

• augmentation (bool) –

add(*args, **kwargs)

Adds a line to the dataset.

Parameters:

• im (path) – Path to the whole page image

• text (str) – Transcription of the line.

• baseline (list) – A list of coordinates [[x0, y0], …, [xn, yn]].

• boundary (list) – A polygon mask for the line.

encode(codec=None)

Adds a codec to the dataset and encodes all text lines.

Has to be run before sampling from the dataset.

Parameters:

codec (Optional[kraken.lib.codec.PytorchCodec]) –

Return type:

None

no_encode()

Creates an unencoded dataset.

Return type:

None

parse(image, text, baseline, boundary, *args, **kwargs)

Parses a sample for the dataset and returns it.

This function is mainly uses for parallelized loading of training data.

Parameters:

• im (path) – Path to the whole page image

• text (str) – Transcription of the line.

• baseline (list) – A list of coordinates [[x0, y0], …, [xn, yn]].

• boundary (list) – A polygon mask for the line.

• image (Union[str, PIL.Image.Image]) –

class kraken.lib.dataset.GroundTruthDataset(split=F_t.default_split, suffix='.gt.txt', normalization=None, whitespace_normalization=True, reorder=True, im_transforms=transforms.Compose([]), augmentation=False)

Dataset for training a line recognition model.

All data is cached in memory.

Parameters:

• split (Callable[[str], str]) –

• suffix (str) –

• normalization (Optional[str]) –

• whitespace_normalization (bool) –

• reorder (Union[bool, str]) –

• im_transforms (Callable[[Any], torch.Tensor]) –

• augmentation (bool) –

add(*args, **kwargs)

Adds a line-image-text pair to the dataset.

Parameters:

image (str) – Input image path

Return type:

None

encode(codec=None)

Adds a codec to the dataset and encodes all text lines.

Has to be run before sampling from the dataset.

Parameters:

codec (Optional[kraken.lib.codec.PytorchCodec]) –

Return type:

None

no_encode()

Creates an unencoded dataset.

Return type:

None

parse(image, *args, **kwargs)

Parses a sample for this dataset.

This is mostly used to parallelize populating the dataset.

Parameters:

image (str) – Input image path

Return type:

Dict

Helpers

kraken.lib.dataset.compute_error(model, batch)

Computes error report from a model and a list of line image-text pairs.

Parameters:

• model (kraken.lib.models.TorchSeqRecognizer) – Model used for recognition

• validation_set – List of tuples (image, text) for validation

• batch (Dict[str, torch.Tensor]) –

Returns:

A tuple with total number of characters and edit distance across the whole validation set.

Return type:

Tuple[int, int]

kraken.lib.dataset.preparse_xml_data(filenames, format_type='xml', repolygonize=False)

Loads training data from a set of xml files.

Extracts line information from Page/ALTO xml files for training of recognition models.

Parameters:

• filenames (Sequence[Union[str, pathlib.Path]]) – List of XML files.

• format_type (str) – Either page, alto or xml for autodetermination.

• repolygonize (bool) – (Re-)calculates polygon information using the kraken algorithm.

Returns:

text, ‘baseline’: [[x0, y0], …], ‘boundary’: [[x0, y0], …], ‘image’: PIL.Image}.

Return type:

A list of dicts {‘text’

kraken.lib.segmentation module

kraken.lib.segmentation.reading_order(lines, text_direction='lr')

Given the list of lines (a list of 2D slices), computes the partial reading order. The output is a binary 2D array such that order[i,j] is true if line i comes before line j in reading order.

Parameters:

• lines (Sequence[Tuple[slice, slice]]) –

• text_direction (str) –

Return type:

numpy.ndarray

kraken.lib.segmentation.polygonal_reading_order(lines, text_direction='lr', regions=None)

Given a list of baselines and regions, calculates the correct reading order and applies it to the input.

Parameters:

• lines (Sequence) – List of tuples containing the baseline and its polygonization.

• regions (Sequence) – List of region polygons.

• text_direction (str) – Set principal text direction for column ordering. Can be ‘lr’ or ‘rl’

Returns:

A reordered input.

Return type:

Sequence[Tuple[List[Tuple[int, int]], List[Tuple[int, int]]]]

kraken.lib.segmentation.denoising_hysteresis_thresh(im, low, high, sigma)

kraken.lib.segmentation.vectorize_lines(im, threshold=0.17, min_length=5)

Vectorizes lines from a binarized array.

Parameters:

• im (np.ndarray) – Array of shape (3, H, W) with the first dimension being probabilities for (start_separators, end_separators, baseline).

• threshold (float) – Threshold for baseline blob detection.

• min_length (int) – Minimal length of output baselines.

Returns:

[[x0, y0, … xn, yn], [xm, ym, …, xk, yk], … ] A list of lists containing the points of all baseline polylines.

kraken.lib.segmentation.calculate_polygonal_environment(im=None, baselines=None, suppl_obj=None, im_feats=None, scale=None, topline=False)

Given a list of baselines and an input image, calculates a polygonal environment around each baseline.

Parameters:

• im (PIL.Image) – grayscale input image (mode ‘L’)

• baselines (sequence) – List of lists containing a single baseline per entry.

• suppl_obj (sequence) – List of lists containing additional polylines that should be considered hard boundaries for polygonizaton purposes. Can be used to prevent polygonization into non-text areas such as illustrations or to compute the polygonization of a subset of the lines in an image.

• im_feats (numpy.array) – An optional precomputed seamcarve energy map. Overrides data in im. The default map is gaussian_filter(sobel(im), 2).

• scale (tuple) – A 2-tuple (h, w) containing optional scale factors of the input. Values of 0 are used for aspect-preserving scaling. None skips input scaling.

• topline (bool) – Switch to change default baseline location for offset calculation purposes. If set to False, baselines are assumed to be on the bottom of the text line and will be offset upwards, if set to True, baselines are on the top and will be offset downwards. If set to None, no offset will be applied.

Returns:

List of lists of coordinates. If no polygonization could be compute for a baseline None is returned instead.

kraken.lib.segmentation.scale_polygonal_lines(lines, scale)

Scales baselines/polygon coordinates by a certain factor.

Parameters:

• lines (Sequence) – List of tuples containing the baseline and it’s polygonization.

• scale (float or tuple of floats) – Scaling factor

Return type:

Sequence[Tuple[List, List]]

kraken.lib.segmentation.scale_regions(regions, scale)

Scales baselines/polygon coordinates by a certain factor.

Parameters:

• lines (Sequence) – List of tuples containing the baseline and it’s polygonization.

• scale (float or tuple of floats) – Scaling factor

• regions (Sequence[Tuple[List[int], List[int]]]) –

Return type:

Sequence[Tuple[List, List]]

kraken.lib.segmentation.compute_polygon_section(baseline, boundary, dist1, dist2)

Given a baseline, polygonal boundary, and two points on the baseline return the rectangle formed by the orthogonal cuts on that baseline segment. The resulting polygon is not garantueed to have a non-zero area.

The distance can be larger than the actual length of the baseline if the baseline endpoints are inside the bounding polygon. In that case the baseline will be extrapolated to the polygon edge.

Parameters:

• baseline (list) – A polyline ((x1, y1), …, (xn, yn))

• boundary (list) – A bounding polygon around the baseline (same format as baseline).

• dist1 (int) – Absolute distance along the baseline of the first point.

• dist2 (int) – Absolute distance along the baseline of the second point.

Returns:

A sequence of polygon points.

Return type:

List[Tuple[int, int]]

kraken.lib.segmentation.extract_polygons(im, bounds)

Yields the subimages of image im defined in the list of bounding polygons with baselines preserving order.

Parameters:

• im (PIL.Image.Image) – Input image

• bounds (Dict[str, Any]) –

  A list of dicts in baseline: ``` {‘type’: ‘baselines’,

  ’lines’: [{‘baseline’: [[x_0, y_0], … [x_n, y_n]],

  ’boundary’: [[x_0, y_0], … [x_n, y_n]]},

  ….]

  or bounding box format: ``` {‘boxes’: [[x_0, y_0, x_1, y_1], …],

  ’text_direction’: ‘horizontal-lr’}

  ```

Yields:

The extracted subimage

Return type:

PIL.Image.Image

kraken.lib.ctc_decoder

kraken.lib.ctc_decoder.beam_decoder(outputs, beam_size=3)

Translates back the network output to a label sequence using same-prefix-merge beam search decoding as described in [0].

[0] Hannun, Awni Y., et al. “First-pass large vocabulary continuous speech recognition using bi-directional recurrent DNNs.” arXiv preprint arXiv:1408.2873 (2014).

Parameters:

• output – (C, W) shaped softmax output tensor

• beam_size (int) – Size of the beam

• outputs (numpy.ndarray) –

Returns:

A list with tuples (class, start, end, prob). max is the maximum value of the softmax layer in the region.

Return type:

List[Tuple[int, int, int, float]]

kraken.lib.ctc_decoder.greedy_decoder(outputs)

Translates back the network output to a label sequence using greedy/best path decoding as described in [0].

[0] Graves, Alex, et al. “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.” Proceedings of the 23rd international conference on Machine learning. ACM, 2006.

Parameters:

• output – (C, W) shaped softmax output tensor

• outputs (numpy.ndarray) –

Returns:

A list with tuples (class, start, end, max). max is the maximum value of the softmax layer in the region.

Return type:

List[Tuple[int, int, int, float]]

kraken.lib.ctc_decoder.blank_threshold_decoder(outputs, threshold=0.5)

Translates back the network output to a label sequence as the original ocropy/clstm.

Thresholds on class 0, then assigns the maximum (non-zero) class to each region.

Parameters:

• output – (C, W) shaped softmax output tensor

• threshold (float) – Threshold for 0 class when determining possible label locations.

• outputs (numpy.ndarray) –

Returns:

A list with tuples (class, start, end, max). max is the maximum value of the softmax layer in the region.

Return type:

List[Tuple[int, int, int, float]]

kraken.lib.exceptions

class kraken.lib.exceptions.KrakenCodecException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenStopTrainingException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenEncodeException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenRecordException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenInvalidModelException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenInputException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenRepoException(message=None)

Common base class for all non-exit exceptions.

class kraken.lib.exceptions.KrakenCairoSurfaceException(message, width, height)

Raised when the Cairo surface couldn’t be created.

Parameters:

• message (str) –

• width (int) –

• height (int) –

message

Error message

Type:

str

width

Width of the surface

Type:

int

height

Height of the surface

Type:

int

Legacy modules

These modules are retained for compatibility reasons or highly specialized use cases. In most cases their use is not necessary and they aren’t further developed for interoperability with new functionality, e.g. the transcription and line generation modules do not work with the baseline segmenter.

kraken.binarization module

kraken.binarization.nlbin(im, threshold=0.5, zoom=0.5, escale=1.0, border=0.1, perc=80, range=20, low=5, high=90)

Performs binarization using non-linear processing.

Parameters:

• im (PIL.Image.Image) – Input image

• threshold (float) –

• zoom (float) – Zoom for background page estimation

• escale (float) – Scale for estimating a mask over the text region

• border (float) – Ignore this much of the border

• perc (int) – Percentage for filters

• range (int) – Range for filters

• low (int) – Percentile for black estimation

• high (int) – Percentile for white estimation

Returns:

PIL.Image.Image containing the binarized image

Raises:

KrakenInputException – When trying to binarize an empty image.

Return type:

PIL.Image.Image

kraken.transcribe module

class kraken.transcribe.TranscriptionInterface(font=None, font_style=None)

add_page(im, segmentation=None, records=None)

Adds an image to the transcription interface, optionally filling in information from a list of ocr_record objects.

Parameters:

• im (PIL.Image) – Input image

• segmentation (dict) – Output of the segment method.

• records (list) – A list of ocr_record objects.

write(fd)

Writes the HTML file to a file descriptor.

Parameters:

fd (File) – File descriptor (mode=’rb’) to write to.

kraken.linegen module