Merge branch 'main' into ro-fixes and resolve conflicts…

major conflicts resolved manually:

- branches for non-`light` segmentation already removed in main
- Keras/TF setup and no TF1 sessions, esp. in new ModelZoo
- changes to binarizer and its CLI (`mode`, `overwrite`, `run_single()`)
- writer: `build...` w/ kwargs instead of positional
- training for segmentation/binarization/enhancement tasks:
  * drop unused `generate_data_from_folder()`
  * simplify `preprocess_imgs()`: turn `preprocess_img()`, `get_patches()`
    and `get_patches_num_scale_new()` into generators, only writing
    result files in the caller (top-level loop) instead of passing
    output directories and file counter
- training for new OCR task:
  * `train`: put keys into additional `config_params` where they belong,
    resp. (conditioned under existing keys), and w/ better documentation
  * `train`: add new keys as kwargs to `run()` to make usable
  * `utils`: instead of custom data loader `data_gen_ocr()`, re-use
    existing `preprocess_imgs()` (for cfg capture and top-level loop),
    but extended w/ new kwargs and calling new `preprocess_img_ocr()`;
    the latter as single-image generator (also much simplified)
  * `train`: use tf.data loader pipeline from that generator w/ standard
    mechanisms for batching, shuffling, prefetching etc.
  * `utils` and `train`: instead of `vectorize_label`, use `Dataset.padded_batch`
  * add TensorBoard callback and re-use our checkpoint callback
  * also use standard Keras top-level loop for training

still problematic (substantially unresolved):
- `Patches` now only w/ fixed implicit size
  (ignoring training config params)
- `PatchEncoder` now only w/ fixed implicit num patches and projection dim
  (ignoring training config params)

requirements.txt

@@ -7,3 +7,4 @@ tf-keras # avoid keras 3 (also needs TF_USE_LEGACY_KERAS=1)
 numba <= 0.58.1
 scikit-image
 biopython
+tabulate