使用深度学习阅读和分类扫描文档

共 17487字,需浏览 35分钟

 ·

2021-10-22 07:12

点击上方小白学视觉”,选择加"星标"或“置顶

重磅干货,第一时间送达

收集数据


首先,我们要做的第一件事是创建一个简单的数据集,这样我们就可以测试我们工作流程的每一部分。理想情况下,我们的数据集将包含各种易读性和时间段的扫描文档,以及每个文档所属的高级主题。我找不到具有这些精确规格的数据集,所以我开始构建自己的数据集。我决定的高层次话题是政府、信件、吸烟和专利,随机的选择这些主要是因为每个地区都有各种各样的扫描文件。


我从这些来源中的每一个中挑选了 20 个左右的大小合适的文档,并将它们放入由主题定义的单独文件夹中。


经过将近一整天的搜索和编目所有图像后,我们将它们全部调整为 600x800 并将它们转换为 PNG 格式。


简单的调整大小和转换脚本如下:

from PIL import Image
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


构建OCR管道


光学字符识别是从图像中提取文字的过程。这通常是通过机器学习模型完成的,最常见的是通过包含卷积神经网络的管道来完成。虽然我们可以为我们的应用程序训练自定义 OCR 模型,但它需要更多的训练数据和计算资源。相反,我们将使用出色的 Microsoft 计算机视觉 API,其中包括专门用于 OCR 的特定模块。API 调用将使用图像(作为 PIL 图像)并输出几位信息,包括图像上文本的位置/方向作为以及文本本身。以下函数将接收一个 PIL 图像列表并输出一个大小相等的提取文本列表:

def image_to_text(imglist, ndocs=10):    '''    Take in a list of PIL images and return a list of extracted text using OCR    '''    headers = {        # Request headers        'Content-Type': 'application/octet-stream',        'Ocp-Apim-Subscription-Key': 'YOUR_KEY_HERE',    }
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)


后期处理


由于在某些情况下我们可能希望在这里结束我们的工作流程,而不是仅仅将提取的文本作为一个巨大的列表保存在内存中,我们还可以将提取的文本写入与原始输入文件同名的单个 txt 文件中。微软的OCR技术虽然不错,但偶尔也会出错。我们可以使用 SpellChecker 模块减少其中的一些错误,以下脚本接受输入和输出文件夹,读取输入文件夹中的所有扫描文档,使用我们的 OCR 脚本读取它们,运行拼写检查并纠正拼写错误的单词,最后将原始txt文件导出目录。

'''Read in a list of scanned images (as .png files > 50x50px) and output a set of .txt files containing the text content of these scans'''

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")


为建模准备文本


如果我们的扫描文档集足够大,将它们全部写入一个大文件夹会使它们难以分类,并且我们可能已经在文档中进行了某种隐式分组。如果我们大致了解我们拥有多少种不同的“类型”或文档主题,我们可以使用主题建模来帮助自动识别这些。这将为我们提供基础架构,以根据文档内容将 OCR 中识别的文本拆分为单独的文件夹,我们将使用该主题模型被称为LDA。为了运行这个模型,我们需要对我们的数据进行更多的预处理和组织,因此为了防止我们的脚本变得冗长和拥挤,我们将假设已经使用上述工作流程读取了扫描的文档并将其转换为 txt 文件. 然后主题模型将读入这些 txt 文件,将它们分类到我们指定的任意多个主题中,并将它们放入适当的文件夹中。


我们将从一个简单的函数开始,读取文件夹中所有输出的 txt 文件,并将它们读入包含 (filename, text) 的元组列表。

def read_and_return(foldername, fileext='.txt'):  '''  Read all text files with fileext from foldername, and place them into a list of tuples as  [(filename, text), ... , (filename, text)]  '''    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()    return(alltext) # Returns list of tuples [(filename, text), ... (filename,text)]

接下来,我们需要确保所有无用的词(那些不能帮助我们区分特定文档主题的词)。我们将使用三种不同的方法来做到这一点:

  1. 删除停用词

  2. 去除标签、标点、数字和多个空格

  3. TF-IDF 过滤


为了实现所有这些(以及我们的主题模型),我们将使用 Gensim 包。下面的脚本将对文本列表(上述函数的输出)运行必要的预处理步骤并训练 LDA 模型。

from gensim import corpora, models, similaritiesfrom gensim.parsing.preprocessing import remove_stopwords, preprocess_string
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)


使用模型对文档进行分类


一旦我们训练了我们的 LDA 模型,我们就可以使用它来将我们的训练文档集(以及可能出现的未来文档)分类为主题,然后将它们放入适当的文件夹中。

对新的文本字符串使用经过训练的 LDA 模型需要一些麻烦,所有的复杂性都包含在下面的函数中:

def find_topic(textlist, 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 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,我们需要另一种方法来根据主题索引获取主题的实际名称:

def topic_label(ldamodel, topicnum):    alltopics = ldamodel.show_topics(formatted=False)    topic = alltopics[topicnum]    topic = dict(topic[1])    return(max(topic, key=lambda key: topic[key]))


现在,我们可以将上面编写的所有函数粘贴到一个接受输入文件夹、输出文件夹和主题计数的脚本中。该脚本将读取输入文件夹中所有扫描的文档图像,将它们写入txt 文件,构建LDA 模型以查找文档中的高级主题,并根据文档主题将输出的txt 文件归类到文件夹中。

################################################################## This script takes in an input folder of scanned documents     ## and reads these documents, seperates them into topics         ## and outputs raw .txt files into the output folder, seperated  ## by topic                                                      ##################################################################
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 operator return(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 files for 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 + "//" + 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 names for 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 folders for 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")


本文代码Github链接:

https://github.com/ShairozS/Scan2Topic


好消息,小白学视觉团队的知识星球开通啦,为了感谢大家的支持与厚爱,团队决定将价值149元的知识星球现时免费加入。各位小伙伴们要抓住机会哦!


下载1:OpenCV-Contrib扩展模块中文版教程
在「小白学视觉」公众号后台回复:扩展模块中文教程即可下载全网第一份OpenCV扩展模块教程中文版,涵盖扩展模块安装、SFM算法、立体视觉、目标跟踪、生物视觉、超分辨率处理等二十多章内容。

下载2:Python视觉实战项目52讲
小白学视觉公众号后台回复:Python视觉实战项目即可下载包括图像分割、口罩检测、车道线检测、车辆计数、添加眼线、车牌识别、字符识别、情绪检测、文本内容提取、面部识别等31个视觉实战项目,助力快速学校计算机视觉。

下载3:OpenCV实战项目20讲
小白学视觉公众号后台回复:OpenCV实战项目20讲即可下载含有20个基于OpenCV实现20个实战项目,实现OpenCV学习进阶。

交流群


欢迎加入公众号读者群一起和同行交流,目前有SLAM、三维视觉、传感器自动驾驶、计算摄影、检测、分割、识别、医学影像、GAN算法竞赛等微信群(以后会逐渐细分),请扫描下面微信号加群,备注:”昵称+学校/公司+研究方向“,例如:”张三 + 上海交大 + 视觉SLAM“。请按照格式备注,否则不予通过。添加成功后会根据研究方向邀请进入相关微信群。请勿在群内发送广告,否则会请出群,谢谢理解~


浏览 34
点赞
评论
收藏
分享

手机扫一扫分享

分享
举报
评论
图片
表情
推荐
点赞
评论
收藏
分享

手机扫一扫分享

分享
举报