Performs OCR on a list of images using Tesseract and performs fuzzy string matching with a given list of strings.
This function takes a path to the Tesseract OCR executable, a list of image paths or URLs,
a list of strings to compare against the recognized text, and optional fuzzy matching settings.
It returns a pandas DataFrame with OCR results and fuzzy matching scores.
Args:
tesseract_path (str): Path to the Tesseract OCR executable.
allpics (Union[list, tuple]): List of image paths, URLs, or other image data sources.
strings_to_compare (Union[list, tuple, np.ndarray]): List of strings for fuzzy matching.
compare_single_words (bool, optional): Enable fuzzy matching on individual words.
Defaults to True.
compare_grouped_words (bool, optional): Enable fuzzy matching on grouped words.
Defaults to True.
scorer_single_words (valid_scorer, optional): Fuzzy matching scorer for single words.
Defaults to "WRatio".
scorer_grouped_words (valid_scorer, optional): Fuzzy matching scorer for grouped words.
Defaults to "WRatio".
add_after_tesseract_path (str, optional): Additional arguments for Tesseract after
the input image path. Defaults to an empty string.
add_at_the_end (str, optional): Additional arguments to append to the Tesseract command.
Defaults to "-l eng --psm 3".
**kwargs: Additional keyword arguments to control the fuzzy matching process.
Returns:
pd.DataFrame: A DataFrame with OCR results and fuzzy matching scores, including columns:
- 'id_img': Image ID
- 'id_word': Word ID within the image
- 'ocr_result': Recognized text
- 'start_x': Starting X-coordinate of the bounding box
- 'end_x': Ending X-coordinate of the bounding box
- 'start_y': Starting Y-coordinate of the bounding box
- 'end_y': Ending Y-coordinate of the bounding box
- 'conf': Confidence score
- 'grouped_text': Grouped text for fuzzy matching
- 'compared_grouped_words_similarity': Fuzzy matching score for grouped words
- 'compared_grouped_words_index': Index of the matched string for grouped words
- 'compared_grouped_words_value': Matched value for grouped words
- 'compared_single_words_similarity': Fuzzy matching score for single words
- 'compared_single_words_index': Index of the matched string for single words
- 'compared_single_words_value': Matched value for single words
Example:
import re
from tesseractrapidfuzz import ocr_and_fuzzy_check
df = ocr_and_fuzzy_check(
tesseract_path=r"C:\Program Files\Tesseract-OCR\tesseract.exe",
allpics=[
"https://m.media-amazon.com/images/I/711y6oE2JrL._SL1500_.jpg",
"https://m.media-amazon.com/images/I/61g+KBpG20L._SL1500_.jpg",
],
strings_to_compare=[
"nonviolent",
"communication",
"emotional",
"well-being",
"terrible",
"today.",
"discover",
"definitive",
"guides",
"transforming",
"converting",
"conflict",
"meaningful",
"connection,",
"unveiling",
"inspirational",
"strategies",
"engagement.",
"martha",
"williams",
"nonviolent communication",
"emotional well-being",
"I had a terrible day at work today.",
"wait till you",
"heared about",
"the art of nonviolent communication",
"martha a. williams",
],
compare_single_words=True,
compare_grouped_words=True,
scorer_single_words="QRatio",
scorer_grouped_words="WRatio",
add_after_tesseract_path="",
add_at_the_end="-l eng --psm 3",
workers=5,
processor=lambda x: re.sub(r"\W+", "", str(x).lower()),
)
print(df.to_string())
# ...
# 7 1 8 terrible 448 563 371 396 77 2875 505 383 115 25 2 | had a terrible 100.000000 4 terrible 90.0 4 terrible
# 8 1 9 day 363 418 415 448 96 1815 390 431 55 33 3 day at work 75.000000 5 today. 90.0 22 I had a terrible day at work today.
# 9 1 10 at 427 457 418 440 96 660 442 429 30 22 3 day at work 50.000000 18 martha 90.0 22 I had a terrible day at work today.
# 10 1 11 work 466 540 415 440 96 1850 503 427 74 25 3 day at work 33.333332 6 discover 90.0 22 I had a terrible day at work today.
# 11 1 12 today. 402 498 460 492 96 3072 450 476 96 32 4 today. 100.000000 5 today. 100.0 5 today.
# 12 1 13 Wait 551 635 525 556 95 2604 593 540 84 31 5 Wait till you 53.333332 23 wait till you 100.0 23 wait till you
# 13 1 14 till 645 695 525 556 96 1550 670 540 50 31 5 Wait till you 53.333332 23 wait till you 100.0 23 wait till you
# 14 1 15 you 705 773 533 565 96 2176 739 549 68 32 5 Wait till you 42.857143 23 wait till you 100.0 23 wait till you
# 15 1 16 hear 562 645 579 610 95 2573 603 594 83 31 6 hear about 53.333332 24 heared about 90.0 24 heared about
# 16 1 17 about 663 767 579 610 96 3224 715 594 104 31 6 hear about 62.500000 24 heared about 90.0 24 heared about
# 17 2 1 ART 94 246 125 207 95 12464 170 166 152 82 7 ART OF NONVIOLENT 66.666664 18 martha 90.0 0 nonviolent
# 18 2 2 OF 275 376 125 207 95 8282 325 166 101 82 7 ART OF NONVIOLENT 40.000000 11 conflict 90.0 0 nonviolent
# 19 2 3 NONVIOLENT 407 907 125 206 96 40500 657 165 500 81 7 ART OF NONVIOLENT 100.000000 0 nonviolent 90.0 0 nonviolent
# 20 2 4 COMMUNICATION 167 832 296 377 96 53865 499 336 665 81 8 COMMUNICATION 100.000000 1 communication 100.0 1 communication
# 21 2 5 TAR 319 379 428 444 31 960 349 436 60 16 9 TAR 50.000000 5 today. 72.0 9 transforming
# 22 2 6 DISCOVER 192 307 624 667 96 4945 249 645 115 43 10 DISCOVER THE DEFINITIVE GUIDES TO NONVIOLENT 100.000000 6 discover 90.0 0 nonviolent
# 23 2 7 THE 320 360 624 667 96 1720 340 645 40 43 10 DISCOVER THE DEFINITIVE GUIDES TO NONVIOLENT 44.444443 18 martha 90.0 0 nonviolent
# 24 2 8 DEFINITIVE 374 507 624 667 96 5719 440 645 133 43 10 DISCOVER THE DEFINITIVE GUIDES TO NONVIOLENT 100.000000 7 definitive 90.0 0 nonviolent
# 25 2 9 GUIDES 521 604 624 667 96 3569 562 645 83 43 10 DISCOVER THE DEFINITIVE GUIDES TO NONVIOLENT 100.000000 8 guides 90.0 0 nonviolent
# 26 2 10 TO 618 645 628 654 96 702 631 641 27 26 10 DISCOVER THE DEFINITIVE GUIDES TO NONVIOLENT 57.142857 5 today. 90.0 0 nonviolent
# 27 2 11 NONVIOLENT 661 810 624 667 96 6407 735 645 149 43 10 DISCOVER THE DEFINITIVE GUIDES TO NONVIOLENT 100.000000 0 nonviolent 90.0 0 nonviolent
# ...
Note:
- The function combines OCR results with fuzzy string matching, allowing for versatile text analysis.
- Valid_scoring options are: "WRatio", "QRatio", "ratio", "partial_ratio".