def build_candidates_list(token, anagrams_list, ocr_sims_list, structures):
"""Merge anagram and OCRkey list into one list.
Parameters:
token (:func:`str`): Cleaned token
anagrams_list (:func:`dict`): Result of `select_anagrams`
ocr_sims_list (:func:`dict`): Result of `select_ocrsims`
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Correction tokens (keys) along with their score (values)
"""
final_list = anagrams_list
ocr_list = truncate_ocr_sim_list(token, ocr_sims_list)
strong_ocr_list = ocr_list
weak_ocr_list = {}
if len(ocr_list) > 5:
(strong_ocr_list, weak_ocr_list) = split_ocr_list(token, ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word in final_list.keys():
final_list[ocr_word] *= ocr_score
del strong_ocr_list[ocr_word]
strong_ocr_list.update(weak_ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word not in final_list.keys():
final_list[ocr_word] = rate_anagram(structures["occurence_map"], token, ocr_word, 1) \
* rate_ocr_key(structures["occurence_map"], token, ocr_word, 0)
return final_list
def build_candidates_list(token, anagrams_list, ocr_sims_list, structures):
"""Merge anagram and OCRkey list into one list.
Parameters:
token (:func:`str`): Cleaned token
anagrams_list (:func:`dict`): Result of `select_anagrams`
ocr_sims_list (:func:`dict`): Result of `select_ocrsims`
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Correction tokens (keys) along with their score (values)
"""
final_list = anagrams_list
ocr_list = truncate_ocr_sim_list(token, ocr_sims_list)
strong_ocr_list = ocr_list
weak_ocr_list = {}
if len(ocr_list) > 5:
(strong_ocr_list, weak_ocr_list) = split_ocr_list(token, ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word in final_list.keys():
final_list[ocr_word] *= ocr_score
del strong_ocr_list[ocr_word]
strong_ocr_list.update(weak_ocr_list)
for ocr_word, ocr_score in strong_ocr_list.items():
if ocr_word not in final_list.keys():
final_list[ocr_word] = rate_anagram(structures["occurence_map"], token, ocr_word, 1) \
* rate_ocr_key(structures["occurence_map"], token, ocr_word, 0)
return final_list
def select_ocrsims(token, structures):
"""Select similar words for a given token
Parameters:
token (:func:`str`): Cleaned token
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Similar words (keys) along with their score (values)
"""
delta = 2
ocr_sims = {}
word_hash = ocr_key_hash(token)
sim_hash_list = {
} # Using a dictionary avoid multiple entries if a key is retrieved twice
key_index = -1
# for (key, value) in word_hash:
for key, value in word_hash:
key_index += 1
sim_hash = deepcopy(word_hash)
for d in range(-delta, delta + 1):
if d != 0:
card = max(int(value) + d, 1)
sim_hash[key_index] = (key, card)
# Rebuild OCR key string
sim_hash_str = ""
for k, v in sim_hash:
sim_hash_str += k + str(v)
if sim_hash_str in structures["ocrkeys"]:
card_diff = abs(int(value) - card)
sim_hash_list[sim_hash_str] = [
(sim_word, card_diff)
for sim_word in structures["ocrkeys"][sim_hash_str]
if edit_distance(sim_word, token) <= 2
]
for sim_hash_str, sim_list in sim_hash_list.items():
for sim_word, card_diff in sim_list:
sim_score = rate_ocr_key(structures["occurence_map"], token,
sim_word, card_diff)
if sim_score > 0:
ocr_sims[sim_word] = sim_score
return ocr_sims
def select_ocrsims(token, structures):
"""Select similar words for a given token
Parameters:
token (:func:`str`): Cleaned token
structures (:func:`dict`): Datastructures from file
Returns:
:func:`dict` - Similar words (keys) along with their score (values)
"""
delta = 2
ocr_sims = {}
word_hash = ocr_key_hash(token)
sim_hash_list = {} # Using a dictionary avoid multiple entries if a key is retrieved twice
key_index = -1
# for (key, value) in word_hash:
for key, value in word_hash:
key_index += 1
sim_hash = deepcopy(word_hash)
for d in range(-delta, delta+1):
if d != 0:
card = max(int(value)+d, 1)
sim_hash[key_index] = (key, card)
# Rebuild OCR key string
sim_hash_str = ""
for k, v in sim_hash:
sim_hash_str += k + str(v)
if sim_hash_str in structures["ocrkeys"]:
card_diff = abs(int(value)-card)
sim_hash_list[sim_hash_str] = [(sim_word, card_diff)
for sim_word in structures["ocrkeys"][sim_hash_str]
if edit_distance(sim_word, token) <= 2]
for sim_hash_str, sim_list in sim_hash_list.items():
for sim_word, card_diff in sim_list:
sim_score = rate_ocr_key(structures["occurence_map"], token, sim_word, card_diff)
if sim_score > 0:
ocr_sims[sim_word] = sim_score
return ocr_sims
