from PyPDF2 import PdfReader
#早期版本里叫PdfFileReader，已经过时，改名为PdfReader了，见：https://pypdf2.readthedocs.io/en/latest/_modules/PyPDF2/_reader.html?highlight=PdfFileReader#
reader = PdfReader(pdf_path)
number_of_pages = len(reader.pages)
#1.28.0版本之前用numPages，已经过时，见：https://pypdf2.readthedocs.io/en/latest/modules/PdfReader.html#PyPDF2.PdfReader.numPages
print(number_of_pages)  #打印页数
page = reader.pages[0]
#1.28.0版本之前用getPage(pageNumber)，已经过时，见：https://pypdf2.readthedocs.io/en/latest/modules/PdfReader.html#PyPDF2.PdfReader.getPage
print(page)  #打印“PDF第一页”这个Page<PyPDF2._page.Page>对象
text = page.extract_text()
#1.28.0版本之前用extractText()，已经过时，见：https://pypdf2.readthedocs.io/en/latest/modules/PageObject.html#PyPDF2._page.PageObject.extractText
print(text)  #提取出第一页的文字

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{'/Contents': IndirectObject(13, 0), '/Parent': IndirectObject(1, 0), '/Type': '/Page', '/Resources': IndirectObject(14, 0), '/MediaBox': [0, 0, 612, 792]}
ImageNet Classication with Deep Convolutional
Neural Networks
Alex Krizhevsky
University of Toronto
kriz@cs.utoronto.ca
Ilya Sutskever
University of Toronto
ilya@cs.utoronto.ca
Geoffrey E. Hinton
University of Toronto
hinton@cs.utoronto.ca
Abstract
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of ve convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a nal 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efcient GPU implemen-
tation of the convolution operation. To reduce overtting in the fully-connected
layers we employed a recently-developed regularization method called fidropoutfl
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
1 Introduction
Current approaches to object recognition make essential use of machine learning methods. To im-
prove their performance, we can collect larger datasets, learn more powerful models, and use bet-
ter techniques for preventing overtting. Until recently, datasets of labeled images were relatively
small Š on the order of tens of thousands of images (e.g., NORB [16], Caltech-101/256 [8, 9], and
CIFAR-10/100 [12]). Simple recognition tasks can be solved quite well with datasets of this size,
especially if they are augmented with label-preserving transformations. For example, the current-
best error rate on the MNIST digit-recognition task (<0.3%) approaches human performance [4].
But objects in realistic settings exhibit considerable variability, so to learn to recognize them it is
necessary to use much larger training sets. And indeed, the shortcomings of small image datasets
have been widely recognized (e.g., Pinto et al. [21]), but it has only recently become possible to col-
lect labeled datasets with millions of images. The new larger datasets include LabelMe [23], which
consists of hundreds of tho usands of fully-segmented images, and ImageNet [6], which consists of
over 15 million labeled high-resolution images in over 22,000 categories.
To learn about thousands of objects from millions of images, we need a model with a large learning
capacity. However, the immense complexity of the object recognition task means that this prob-
lem cannot be specied even by a dataset as large as ImageNet, so our model should also have lots
of prior knowledge to compensate for all the data we don't have. Convolutional neural networks
(CNNs) constitute one such class of models [16, 11, 13, 18, 15, 22, 26]. Their capacity can be con-
trolled by varying their depth and breadth, and they also make strong and mostly correct assumptions
about the nature of images (namely, stationarity of statistics and locality of pixel dependencies).
Thus, compared to standard feedforward neural networks with similarly-sized layers, CNNs have
much fewer connections and parameters and so they are easier to train, while their theoretically-best
performance is likely to be only slightly worse.
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