- `training.models.CTCDecoder`: switch back
from `tf.nn.ctc_beam_search_decoder()`
to `tf.nn.ctc_greedy_decoder()`
(because ONNX only implements `CTCGreedyDecoder`)
- `training.models.cnn_rnn_ocr_model(inference=True)` and
`training.models.cnn_rnn_ocr_model4inference`:
drop layer `tf.io.decode_raw()`
(because ONNX does not implement `DecodePaddedRaw`)
- `Eynollah_ocr.run_cnn()`: expect bytes arrays from predictor
instead of uint8
- `predictor`: to prevent segfaults when sending `tf.string` results
via `shared_memory`, convert `np.object` to `np.bytes_` directly
when rebuilding the inference model for cnn-rnn-ocr,
- open the old `characters_org.txt` file for the charset
- use it to pass the actual `n_classes` (overriding the config)
- use its path to pass the `characters_txt_file` (overriding the config)
Because transformers v4 and v5 API for image preprocessor differs,
and the model-internal image input sizes are actually irrelevant,
because the preprocessor will resize them anyway, and there is no
batch dimension (because the input images will have different shapes),
do not advertise this information in `.input_shape`.
- ModelZoo: drop `trocr_processor` model type
- `ModelZoo.load_models()`: use Predictor for `ocr_tr` models, too
- `ModelZoo.load_model()`: for `ocr_tr`, load processor and model,
then define a function object as stand-in for the common model
interface based on Keras (w/ `.predict_on_batch()`)
- Predictor: allow multi-input without actual batch dimension
for `ocr_tr` models (because the model takes a list of original
image arrays and resizes them to model shape internally)
- Eynollah_ocr: adapt (replacing preprocessing, prediction and
decoding steps by a single `.predict()` call)
- `min_conf_value_of_textline_text`: apply by skipping
lines below threshold (instead of writing empty text),
and delete their TextEquiv (if existing)
- `write_ocr()`: simplify, and ensure consistency between
line and region level text correctly
- drop `end_character` mechanics and `characters` model type
for decoding output probability (not needed)
- drop `decode_batch_predictions()` and `num_to_char` model type
(part of inference model)
- drop roughshot confidence estimation calculation
(returned precisely by inference model)
- adapt model prediction to inference model: just omit zeros,
map to bytes, filter OOV tokens and decode UTF-8 to str
- if no binarization input was provided, then compute it on the fly
using `binarization` model
- also apply `min_conf_value_of_textline_text` (as for TrOCR)
- batching over entire page instead of region-wise
(which underfilled batches)
- simplify and avoid copied redundant code
- rename `extracted_conf_value_merged` → `extracted_confs_merged`
- move `batched()` from `utils.utils_ocr` to `utils`
- drop `utils_ocr.distortion_free_resize()` (not needed)
- simplify `utils_ocr.break_curved_line_into_small_pieces_and_then_merge()`
- drop `utils_ocr.return_textline_contour_with_added_box_coordinate()`
and `utils_ocr.return_rnn_cnn_ocr_of_given_textlines()` (not needed)
