使用深度學(xué)習(xí)閱讀和分類掃描文檔
時(shí)間：2021-10-27 13:49:43
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[導(dǎo)讀]作者|小白來(lái)源|小白學(xué)視覺(jué)收集數(shù)據(jù)首先，我們要做的第一件事是創(chuàng)建一個(gè)簡(jiǎn)單的數(shù)據(jù)集，這樣我們就可以測(cè)試我們工作流程的每一部分。理想情況下，我們的數(shù)據(jù)集將包含各種易讀性和時(shí)間段的掃描文檔，以及每個(gè)文檔所屬的高級(jí)主題。我找不到具有這些精確規(guī)格的數(shù)據(jù)集，所以我開(kāi)始構(gòu)建自己的數(shù)據(jù)集。我決定...
img_folder = r'F:\Data\Imagery\OCR' # Folder containing topic folders (i.e "News", "Letters" ..etc.)
for subfol in os.listdir(img_folder): # For each of the topic folders sfpath = os.path.join(img_folder, subfol)for imgfile in os.listdir(sfpath): # Get all images in the topic imgpath = os.path.join(sfpath, imgfile) img = Image.open(imgpath) # Read in the image with Pillow img = img.resize((600,800)) # Resize the image newip = imgpath[0:-4] ".png" # Convert to PNG img.save(newip) # Save
params = urllib.parse.urlencode({# Request parameters'language': 'en','detectOrientation ': 'true', })
outtext = [] docnum = 0
for cropped_image in imglist: print("Processing document -- ", str(docnum))# Cropped image must have both height and width > 50 px to run Computer Vision API#if (cropped_image.height or cropped_image.width) < 50:# cropped_images_ocr.append("N/A")# continue ocr_image = cropped_image imgByteArr = io.BytesIO() ocr_image.save(imgByteArr, format='PNG') imgByteArr = imgByteArr.getvalue()
try: conn = http.client.HTTPSConnection('westus.api.cognitive.microsoft.com') conn.request("POST", "/vision/v1.0/ocr?%s" % params, imgByteArr, headers) response = conn.getresponse() data = json.loads(response.read().decode("utf-8"))
curr_text = []for r in data['regions']:for l in r['lines']:for w in l['words']: curr_text.append(str(w['text'])) conn.close() except Exception as e: print("Could not process image
outtext.append(' '.join(curr_text)) docnum = 1
return(outtext)
from functions import preprocess, image_to_textfrom PIL import Imageimport osfrom spellchecker import SpellCheckerimport matplotlib.pyplot as plt
INPUT_FOLDER = r'F:\Data\Imagery\OCR2\Images'OUTPUT_FOLDER = r'F:\Research\OCR\Outputs\AllDocuments'
## First, read in all the scanned document images into PIL imagesscanned_docs_path = os.listdir(INPUT_FOLDER)scanned_docs_path = [x for x in scanned_docs_path if x.endswith('.png')]scanned_docs = [Image.open(os.path.join(INPUT_FOLDER, path)) for path in scanned_docs_path]
## Second, utilize Microsoft CV API to extract text from these images using OCRscanned_docs_text = image_to_text(scanned_docs)
## Third, remove mis-spellings that might have occured from bad OCR readingsspell = SpellChecker()for i in range(len(scanned_docs_text)): clean = scanned_docs_text[i] misspelled = spell.unknown(clean) clean = clean.split(" ")for word in range(len(clean)):if clean[word] in misspelled: clean[word] = spell.correction(clean[word])# Get the one `most likely` answer clean = ' '.join(clean) scanned_docs_text[i] = clean
## Fourth, write the extracted text to individual .txt files with the same name as input filesfor k in range(len(scanned_docs_text)): # For each scanned document
text = scanned_docs_text[k] path = scanned_docs_path[k] # Get the corresponding input filename
text_file_path = path[:-4] ".txt" # Create the output text file text_file = open(text_file_path, "wt")
n = text_file.write(text) # Write the text to the ouput text file
text_file.close()
print("Done")
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刪除停用詞
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去除標(biāo)簽、標(biāo)點(diǎn)、數(shù)字和多個(gè)空格
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TF-IDF 過(guò)濾
def preprocess(document): clean = remove_stopwords(document) clean = preprocess_string(document) return(clean)
def run_lda(textlist, num_topics=10, preprocess_docs=True):
''' Train and return an LDA model against a list of documents '''if preprocess_docs: doc_text = [preprocess(d) for d in textlist]
dictionary = corpora.Dictionary(doc_text) corpus = [dictionary.doc2bow(text) for text in doc_text] tfidf = models.tfidfmodel.TfidfModel(corpus) transformed_tfidf = tfidf[corpus]
lda = models.ldamulticore.LdaMulticore(transformed_tfidf, num_topics=num_topics, id2word=dictionary)
return(lda, dictionary)
For each query ( document in the test file) , tokenize the query, create a feature vector just like how it was done while training and create text_corpus '''
text_corpus = []
for query in textlist: temp_doc = tokenize(query.strip()) current_doc = [] temp_doc = list(temp_doc)for word in range(len(temp_doc)): current_doc.append(temp_doc[word])
text_corpus.append(current_doc)''' For each feature vector text, lda[doc_bow] gives the topic distribution, which can be sorted in descending order to print the very first topic ''' tops = []for text in text_corpus: doc_bow = dictionary.doc2bow(text) topics = sorted(lda[doc_bow],key=lambda x:x[1],reverse=True)[0] tops.append(topics)return(tops) 最后，我們需要另一種方法來(lái)根據(jù)主題索引獲取主題的實(shí)際名稱：
import osfrom PIL import Imageimport base64import http.client, urllib.request, urllib.parse, urllib.error, base64import ioimport jsonimport requestsimport urllibfrom gensim import corpora, models, similaritiesfrom gensim.utils import tokenizefrom gensim.parsing.preprocessing import remove_stopwords, preprocess_stringimport httpimport shutilimport tqdm
def filter_for_english(text): dict_url = 'https://raw.githubusercontent.com/first20hours/' \'google-10000-english/master/20k.txt'
dict_words = set(requests.get(dict_url).text.splitlines())
english_words = tokenize(text) english_words = [w for w in english_words if w in list(dict_words)] english_words = [w for w in english_words if (len(w)>1 or w.lower()=='i')]return(' '.join(english_words))
def preprocess(document): clean = filter_for_english(document) clean = remove_stopwords(clean) clean = preprocess_string(clean)
# Remove non-english words
return(clean)
def read_and_return(foldername, fileext='.txt', delete_after_read=False): allfiles = os.listdir(foldername) allfiles = [os.path.join(foldername, f) for f in allfiles if f.endswith(fileext)] alltext = []for filename in allfiles:with open(filename, 'r') as f: alltext.append((filename, f.read())) f.close()if delete_after_read: os.remove(filename)return(alltext) # Returns list of tuples [(filename, text), ... (filename,text)]
def image_to_text(imglist, ndocs=10):''' Take in a list of PIL images and return a list of extracted text ''' headers = {# Request headers'Content-Type': 'application/octet-stream','Ocp-Apim-Subscription-Key': '89279deb653049078dd18b1b116777ea', }
params = urllib.parse.urlencode({# Request parameters'language': 'en','detectOrientation ': 'true', })
outtext = [] docnum = 0
for cropped_image in tqdm.tqdm(imglist, total=len(imglist)):# Cropped image must have both height and width > 50 px to run Computer Vision API#if (cropped_image.height or cropped_image.width) < 50:# cropped_images_ocr.append("N/A")# continue ocr_image = cropped_image imgByteArr = io.BytesIO() ocr_image.save(imgByteArr, format='PNG') imgByteArr = imgByteArr.getvalue()
try: conn = http.client.HTTPSConnection('westus.api.cognitive.microsoft.com') conn.request("POST", "/vision/v1.0/ocr?%s" % params, imgByteArr, headers) response = conn.getresponse() data = json.loads(response.read().decode("utf-8"))
curr_text = []for r in data['regions']:for l in r['lines']:for w in l['words']: curr_text.append(str(w['text'])) conn.close()except Exception as e: print("[Errno {0}] {1}".format(e.errno, e.strerror))
outtext.append(' '.join(curr_text)) docnum = 1
return(outtext)
def run_lda(textlist, num_topics=10, return_model=False, preprocess_docs=True):
''' Train and return an LDA model against a list of documents '''if preprocess_docs: doc_text = [preprocess(d) for d in textlist] dictionary = corpora.Dictionary(doc_text)
corpus = [dictionary.doc2bow(text) for text in doc_text] tfidf = models.tfidfmodel.TfidfModel(corpus) transformed_tfidf = tfidf[corpus]
lda = models.ldamulticore.LdaMulticore(transformed_tfidf, num_topics=num_topics, id2word=dictionary)
input_doc_topics = lda.get_document_topics(corpus)
return(lda, dictionary)
def find_topic(text, dictionary, lda):''' https://stackoverflow.com/questions/16262016/how-to-predict-the-topic-of-a-new-query-using-a-trained-lda-model-using-gensim
For each query ( document in the test file) , tokenize the query, create a feature vector just like how it was done while training and create text_corpus '''
text_corpus = []
for query in text: temp_doc = tokenize(query.strip()) current_doc = [] temp_doc = list(temp_doc)for word in range(len(temp_doc)): current_doc.append(temp_doc[word])
text_corpus.append(current_doc)''' For each feature vector text, lda[doc_bow] gives the topic distribution, which can be sorted in descending order to print the very first topic ''' tops = []for text in text_corpus: doc_bow = dictionary.doc2bow(text) topics = sorted(lda[doc_bow],key=lambda x:x[1],reverse=True)[0] tops.append(topics)return(tops)
def topic_label(ldamodel, topicnum): alltopics = ldamodel.show_topics(formatted=False) topic = alltopics[topicnum] topic = dict(topic[1])import operatorreturn(max(topic, key=lambda key: topic[key]))
INPUT_FOLDER = r'F:/Research/OCR/Outputs/AllDocuments'OUTPUT_FOLDER = r'F:/Research/OCR/Outputs/AllDocumentsByTopic'TOPICS = 4
if __name__ == '__main__':
print("Reading scanned documents")## First, read in all the scanned document images into PIL images scanned_docs_fol = r'F:/Research/OCR/Outputs/AllDocuments' scanned_docs_path = os.listdir(scanned_docs_fol) scanned_docs_path = [os.path.join(scanned_docs_fol, p) for p in scanned_docs_path] scanned_docs = [Image.open(x) for x in scanned_docs_path if x.endswith('.png')]
## Second, utilize Microsoft CV API to extract text from these images using OCR scanned_docs_text = image_to_text(scanned_docs)
print("Post-processing extracted text")## Third, remove mis-spellings that might have occured from bad OCR readings spell = SpellChecker()for i in range(len(scanned_docs_text)): clean = scanned_docs_text[i] misspelled = spell.unknown(clean) clean = clean.split(" ")for word in range(len(clean)):if clean[word] in misspelled: clean[word] = spell.correction(clean[word])# Get the one `most likely` answer clean = ' '.join(clean) scanned_docs_text[i] = clean
print("Writing read text into files")## Fourth, write the extracted text to individual .txt files with the same name as input filesfor k in range(len(scanned_docs_text)): # For each scanned document
text = scanned_docs_text[k] text = filter_for_english(text) path = scanned_docs_path[k] # Get the corresponding input filename path = path.split("\\")[-1] text_file_path = OUTPUT_FOLDER "http://" path[0:-4] ".txt" # Create the output text file text_file = open(text_file_path, "wt")
n = text_file.write(text) # Write the text to the ouput text file
text_file.close()
# First, read all the output .txt files print("Reading files") texts = read_and_return(OUTPUT_FOLDER)
print("Building LDA topic model")# Second, train the LDA model (pre-processing is internally done) print("Preprocessing Text") textlist = [t[1] for t in texts] ldamodel, dictionary = run_lda(textlist, num_topics=TOPICS)
# Third, extract the top topic for each document print("Extracting Topics") topics = []for t in texts: topics.append((t[0], find_topic([t[1]], dictionary, ldamodel)))
# Convert topics to topic namesfor i in range(len(topics)): topnum = topics[i][1][0][0]#print(topnum) topics[i][1][0] = topic_label(ldamodel, topnum)# [(filename, topic), ..., (filename, topic)]
# Create folders for the topics print("Copying Documents into Topic Folders") foundtopics = []for t in topics: foundtopics = t[1] foundtopics = set(foundtopics) topicfolders = [os.path.join(OUTPUT_FOLDER, f) for f in foundtopics] topicfolders = set(topicfolders) [os.makedirs(m) for m in topicfolders]
# Copy files into appropriate topic foldersfor t in topics: filename, topic = t src = filename filename = filename.split("\\") dest = os.path.join(OUTPUT_FOLDER, topic[0]) dest = dest "/" filename[-1] copystr = "copy " src " " dest shutil.copyfile(src, dest) os.remove(src)
print("Done")
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