class_docstr: Implements an object able to identify character sequences in images
  
  Args:
  ----
      pre_processor: transform inputs for easier batched model inference
      model: core detection architecture
      split_wide_crops: wether to use crop splitting for high aspect ratio crops
class_docstr_tok: ["Implements","an","object","able","to","identify","character","sequences","in","images","Args",":","--","--","pre_processor",":","transform","inputs","for","easier","batched","model","inference","model",":","core","detection","architecture","split_wide_crops",":","wether","to","use","crop","splitting","for","high","aspect","ratio","crops"]
code: def __init__(
      self,
      pre_processor: PreProcessor,
      model: RecognitionModel,
      split_wide_crops: bool = True,
  ) -> None:
      super().__init__()
      self.pre_processor = pre_processor
      self.model = model
      self.split_wide_crops = split_wide_crops
      self.critical_ar = 8  # Critical aspect ratio
      self.dil_factor = 1.4  # Dilation factor to overlap the crops
      self.target_ar = 6
  
file_path: doctr/doctr/models/recognition/predictor/tensorflow.py
imports: from typing import Any, List, Tuple, Union
  import numpy
  import tensorflow
  from doctr.models.preprocessor import PreProcessor
  from doctr.utils.repr import NestedObject
  from ..core import RecognitionModel
  from ._utils import remap_preds, split_crops
label_desc: Use image node_id 1 to create a new RecognitionPredictor object from inherited base classes: NestedObject with example: obj = RecognitionPredictor(pre_processor, model, split_wide_crops)