class_docstr: Point metric for textspotting. Proposed in SPTS.
  
  Args:
      text_score_thrs (dict): Best text score threshold searching
          space. Defaults to dict(start=0.8, stop=1, step=0.01).
      word_spotting (bool): Whether to work in word spotting mode. Defaults
          to False.
      lexicon_path (str, optional): Lexicon path for word spotting, which
          points to a lexicon file or a directory. Defaults to None.
      lexicon_mapping (tuple, optional): The rule to map test image name to
          its corresponding lexicon file. Only effective when lexicon path
          is a directory. Defaults to ('(.*).jpg', r'.txt').
      pair_path (str, optional): Pair path for word spotting, which points
          to a pair file or a directory. Defaults to None.
      pair_mapping (tuple, optional): The rule to map test image name to
          its corresponding pair file. Only effective when pair path is a
          directory. Defaults to ('(.*).jpg', r'.txt').
      match_dist_thr (float, optional): Matching distance threshold for
          word spotting. Defaults to None.
      collect_device (str): Device name used for collecting results from
          different ranks during distributed training. Must be 'cpu' or
          'gpu'. Defaults to 'cpu'.
      prefix (str, optional): The prefix that will be added in the metric
          names to disambiguate homonymous metrics of different evaluators.
          If prefix is not provided in the argument, self.default_prefix
          will be used instead. Defaults to None
docstr: Check if the text could be in a valid dictionary.
docstr_tok: ["Check","if","the","text","could","be","in","a","valid","dictionary","."]
code: def _include_in_dict(self, text: str) -> bool:
      """Check if the text could be in a valid dictionary."""
      if len(text) != len(text.replace(" ", "")) or len(text) < 3:
          return False
      not_allowed = "×÷·"
      valid_ranges = [
          (ord("a"), ord("z")),
          (ord("A"), ord("Z")),
          (ord("À"), ord("ƿ")),
          (ord("Ǆ"), ord("ɿ")),
          (ord("Ά"), ord("Ͽ")),
          (ord("-"), ord("-")),
      ]
      for char in text:
          code = ord(char)
          if not_allowed.find(char) != -1:
              return False
          valid = any(
              code >= r[0] and code <= r[1] for r in valid_ranges
          )
          if not valid:
              return False
      return True
  
imports: import glob
  import os.path
  import re
  from typing import Dict, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from mmengine.evaluator import BaseMetric
  from mmengine.logging import MMLogger
  from rapidfuzz.distance import Levenshtein
  from shapely.geometry import Point
  from mmocr.registry import METRICS
label_desc: Use image node_id 11 for calling the E2EPointMetric obj's underlying member method code with example usage: obj._include_in_dict(text) and returns: True, False, False, False