get_textpage_ocr(full=False) OCRs vector graphics, producing garbage words #5064

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1. JorjMcKie

   added

   not a bugnot a bug / user error / unable to reproducenot a bug / user error / unable to reproduce

   on Jul 28, 2026
2. JorjMcKie commented on Jul 28, 2026

   

   JorjMcKie

   on Jul 28, 2026

   Collaborator

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   We have indeed changed our approach here: away from iterating over images to looking at the page as a whole. Previously we didn't OCR � when using full=False. Neither did we take into account vectors doing text mimicry.

   For the most part, your problem is caused by Tesseract's over-eagerness: It indeed has a low threshold in assuming that something is worth OCR-ing. It regards general vectors like lines or rectangles much to often. We have no influence here and we also do not support alternative OCR engines in this context.

   The second problem: You are trying to detect OCR need, but your check is incomplete.
   The first page contains only one � out of 676 ok characters. You should consider introducing a more sophisticated check, maybe a percentage-based approach.
   The second page is much different: 573 out of 742 are illegible.

   I recommend considering the following:

   1. Extract text conventionally via get_text("dict"). Execute Tesseract OCR for the bbox of each span that contains a �. Look at this recipe for how to approach this.
   2. Use the page analyzer of pymupdf4llm to see if a page needs OCR. It uses the following logic:

   

   import pymupdf
   import pymupdf4llm
   from pprint import pp
   analyze = pymupdf4llm.ocr.analyze_page.analyze_page
   
   doc = pymupdf.open("ar2024_unknow_unicode.pdf")
   page = doc[0]
   
   pp(analyze(page))
   {'covered': Rect(56.858001708984375, 18.81598663330078, 569.9749755859375, 657.7340698242188),
    'img_joins': 0.23674984093456133,
    'img_area': 0.04145623438648762,
    'txt_joins': 0.8489164265522499,
    'txt_area': 0.08898051574209948,
    'vec_joins': 0.9117148291676324,
    'vec_area': 0.6797198931944479,
    'chars_total': 676,
    'chars_bad': 1,  # 0xFFFD count
    'bad_areas': 0.00015417165507585623,
    'ocr_spans': 0,
    'pixmap': None,
    'vec_norects': 77,  # number of vectors that MIGHT mimic characters
    'needs_ocr': False,  # OCR not recommended
    'reason': None,
    'probability': 0.8193221092224121}  # probability below threshold 93%

   So this function does a detailed analysis and comes to the conclusion that OCR is not needed.
   The result for the second page clearly recommends OCR and gives us the reason why:

   {'covered': Rect(22.25029945373535, 18.81598663330078, 541.2203979492188, 678.5220336914062),
    'img_joins': 0.5118280247708684,
    'img_area': 0.4762521215056628,
    'txt_joins': 0.3988905456292418,
    'txt_area': 0.09699813982510444,  # text area is 9.6% of "covered" area
    'vec_joins': 0.3379046622108768,
    'vec_area': 0.028636319539308345,
    'chars_total': 742,
    'chars_bad': 573,
    'bad_areas': 0.07193382842621195,  # 7% of "covered" area
    'ocr_spans': 0,
    'pixmap': None,
    'vec_norects': 1,
    'needs_ocr': True,  # recommend OCR
    'reason': 'chars_bad',  # for this reason 
    'probability': None}
3. pymupdf

   locked and limited conversation to collaborators on Jul 29, 2026
4. JorjMcKie

   converted this issue into a discussion #5068 on Jul 29, 2026