class_docstr: A text detector using the CRAFT architecture.
  
  Args:
      weights: The weights to use for the model. Currently, only `clovaai_general`
          is supported.
      load_from_torch: Whether to load the weights from the original PyTorch weights.
      optimizer: The optimizer to use for training the model.
      backbone_name: The backbone to use. Currently, only 'vgg' is supported.
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docstr: Recognize the text in a set of images.
  
  Args:
      images: Can be a list of numpy arrays of shape HxWx3 or a list of
          filepaths.
      link_threshold: This is the same as `text_threshold`, but is applied to the
          link map instead of the text map.
      detection_threshold: We want to avoid including boxes that may have
          represented large regions of low confidence text predictions. To do this,
          we do a final check for each word box to make sure the maximum confidence
          value exceeds some detection threshold. This is the threshold used for
          this check.
      text_threshold: When the text map is processed, it is converted from confidence
          (float from zero to one) values to classification (0 for not text, 1 for
          text) using binary thresholding. The threshold value determines the
          breakpoint at which a value is converted to a 1 or a 0. For example, if
          the threshold is 0.4 and a value for particular point on the text map is
          0.5, that value gets converted to a 1. The higher this value is, the less
          likely it is that characters will be merged together into a single word.
          The lower this value is, the more likely it is that non-text will be detected.
          Therein lies the balance.
      size_threshold: The minimum area for a word.
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code: def detect(
      self,
      images: typing.List[typing.Union[np.ndarray, str]],
      detection_threshold=0.7,
      text_threshold=0.4,
      link_threshold=0.4,
      size_threshold=10,
      **kwargs,
  ):
      """Recognize the text in a set of images.
  
      Args:
          images: Can be a list of numpy arrays of shape HxWx3 or a list of
              filepaths.
          link_threshold: This is the same as `text_threshold`, but is applied to the
              link map instead of the text map.
          detection_threshold: We want to avoid including boxes that may have
              represented large regions of low confidence text predictions. To do this,
              we do a final check for each word box to make sure the maximum confidence
              value exceeds some detection threshold. This is the threshold used for
              this check.
          text_threshold: When the text map is processed, it is converted from confidence
              (float from zero to one) values to classification (0 for not text, 1 for
              text) using binary thresholding. The threshold value determines the
              breakpoint at which a value is converted to a 1 or a 0. For example, if
              the threshold is 0.4 and a value for particular point on the text map is
              0.5, that value gets converted to a 1. The higher this value is, the less
              likely it is that characters will be merged together into a single word.
              The lower this value is, the more likely it is that non-text will be detected.
              Therein lies the balance.
          size_threshold: The minimum area for a word.
      """
      images = [compute_input(tools.read(image)) for image in images]
      boxes = getBoxes(
          self.model.predict(np.array(images), **kwargs),
          detection_threshold=detection_threshold,
          text_threshold=text_threshold,
          link_threshold=link_threshold,
          size_threshold=size_threshold,
      )
      return boxes
  
imports: import typing
  import cv2
  import numpy
  import tensorflow
  import efficientnet.tfkeras
  from tensorflow import keras
  from .None import tools
label_desc: Use image node_id 3 for calling the Detector obj's underlying member method code with example usage: obj.detect(images, detection_threshold, text_threshold, link_threshold, size_threshold) and returns: boxes