Extract fields from forms with different structures

Question

I am trying to extract certain fields from the balance. For example, I would like to say that the value of “Inventories” is 1,277,838 for the following balance:

I am currently using Tesseract to convert images to text. However, this transformation leads to a flow of text, so it is difficult to associate fields with their values ​​(since these values ​​are not always next to the text of the corresponding fields).

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Answer 1

, Tesseract Python ( pytesseract). Tesseract.hocr(https://en.wikipedia.org/wiki/HOCR), (, "" ) , Tesseract , . , HOCR Tesseract:

def parse_hocr(search_terms=None, hocr_file=None, regex=None):
    """Parse the hocr file and find a reasonable bounding box for each of the strings
    in search_terms.  Return a dictionary with values as the bounding box to be used for 
    extracting the appropriate text.

    inputs:
        search_terms = Tuple, A tuple of search terms to look for in the HOCR file.

    outputs:
        box_dict = Dictionary, A dictionary whose keys are the elements of search_terms and values
        are the bounding boxes where those terms are located in the document.
    """
    # Make sure the search terms provided are a tuple.
    if not isinstance(search_terms,tuple):
        raise ValueError('The search_terms parameter must be a tuple')

    # Make sure we got a HOCR file handle when called.
    if not hocr_file:
        raise ValueError('The parser must be provided with an HOCR file handle.')

    # Open the hocr file, read it into BeautifulSoup and extract all the ocr words.
    hocr = open(hocr_file,'r').read()
    soup = bs.BeautifulSoup(hocr,'html.parser')
    words = soup.find_all('span',class_='ocrx_word')

    result = dict()

    # Loop through all the words and look for our search terms.        
    for word in words:

        w = word.get_text().lower()

        for s in search_terms:

            # If the word is in our search terms, find the bounding box
            if len(w) > 1 and difflib.SequenceMatcher(None, s, w).ratio() > .5:
                bbox = word['title'].split(';')
                bbox = bbox[0].split(' ')
                bbox = tuple([int(x) for x in bbox[1:]])

                # Update the result dictionary or raise an error if the search term is in there twice.
                if s not in result.keys():
                    result.update({s:bbox})

            else:
                pass

    return result 

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Answer 2

gaw89, Tesseract , . hocr fileformat ( ) , , :

$ tesseract 4LV05.png out -l eng hocr

, , ""

$ grep 'Inventory' out.hocr
 <span class='ocr_line' id='line_1_5' title="bbox 23 183 112 204; baseline 0 -5; x_size 21; x_descenders 5; x_ascenders 4"><span class='ocrx_word' id='word_1_15' title='bbox 23 183 112 204; x_wconf 93'>Inventory</span>

, 183 204 , . , ,

$ grep 'bbox [0-9]* 18[0-9]' out.hocr
<p class='ocr_par' id='par_1_4' lang='eng' title="bbox 23 183 112 204">
 <span class='ocr_line' id='line_1_5' title="bbox 23 183 112 204; baseline 0 -5; x_size 21; x_descenders 5; x_ascenders 4"><span class='ocrx_word' id='word_1_15' title='bbox 23 183 112 204; x_wconf 93'>Inventory</span>
 <span class='ocr_line' id='line_1_30' title="bbox 1082 183 1178 202; baseline 0 -3; x_size 22; x_descenders 5.5; x_ascenders 5.5"><span class='ocrx_word' id='word_1_82' title='bbox 1082 183 1178 202; x_wconf 93'>1,277,838</span>
 <span class='ocr_line' id='line_1_54' title="bbox 1301 183 1379 202; baseline 0 -3; x_size 22; x_descenders 5.5; x_ascenders 5.5"><span class='ocrx_word' id='word_1_107' title='bbox 1301 183 1379 202; x_wconf 95'>953,675</span>

. bbox, .

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