Python OCR libraries for converting PDFs into editable text

PFU Business report

New customer's development
and increasing the sale of product

My country economy at this season keeps escaping from Odoba of business though holds a crude oil
high so on unstable element that continues still, and recovering gradually and well.
In the IT industry, there is an influence such as competing intensification in narrowing investment field.

(The main product and service at this season]

@From the product headquarters
In the image business, the new model turning on of the A3 high-speed, a _bad
two sided color scanner that achieved a high-speed reading aimed at. —
wroom was established in United States, Europe, and Asia/Oceania.

Image business

1) Scanner class

A3 high-speed, two sided color scanner "fi-5900C" that 100 high-n
function to enable industry-leading was installed was announced in
ScanSnap gotten popular because of an office and individual use.

2) DLM solution scanner ~ |
The DLM solution that used received the rise of the concern to efficient Satisfaction rating to new product
management and internal management of the corporate private circum-

stances report in recent years and attracted attention. The function of software that the inspection of data is possible by
the sense that turns over the file is strengthened, and easiness to use has been improved.

{approach on business risk]

@In-house activity

The attestation intended for each office in Shinbashi, Kansai, and Tokai was acquired in environment ISO in February,
2006. In addition, it participates in the minus 6% that is a national movement of the global warming prevention, and
"Culbiz" is done. The scandal of the enterprise has frequently generated is received, concern is sent to the system mainte-
nance including the observance of the law in recent years.

@Enhancement of system of management ‘s

The committee that aimed at the decrease of a variety of business 80

risks in an individual business talk was newly established. Moreover, 70

the recognition of "Privacy mark" is received to manage customer and si

employee's individual information appropriately in 2001, and the activ- ? bP ae

ity based on the protection of individual information policy is continued. 30

lt is .bAsia/Oceania in globalln addition, our technology, commodity 20

power, and correspondence power were evaluating acquired. 10 ti
0

1998 1999 2000 2001 2002 2003 2004

Satisfaction rating to new product

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 'My country economy at this season keeps escaping from Odoba of business though holds a crude oil',
 'high so on unstable element that continues still, and recovering gradually and well,',
 'In the IT industry, there is an influence such as competing intensification in narrowing investment field.',
 '[The main product and service at this season]',
 'From the product headquarters',
 'In the image business; the new model turning on of the A3 high-speed,',
 'usually',
 'bad',
 'two sided color scanner that achieved a high-speed reading aimed at.',
 'wroom was established in United States, Europe, and Asia/Oceania',
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 'that 100 high-n',
 '26%',
 'function to enable industry-leading was installed was announced in',
 'ScanSnap gotten popular because of an office and individual use:',
 '2) DLM solution scanner',
 'The DLM solution that used received the rise of the concern to efficient',
 'Satisfaction rating to new product',
 'management and internal management of the corporate private circum-',
 'stances report in recent years and attracted attention; The function of software that the inspection of data is possible by',
 'the sense that turns over the file is strengthened, and easiness to use has been improved:',
 '[approach on business risk]',
 'In-house activity',
 'The attestation intended for each office in Shinbashi, Kansai , and Tokai was acquired in environment ISO in February,',
 '2006. In addition, it participates in the minus 6% that is a national movement of the global warming prevention, and',
 '"Culbiz" is done. The scandal of the enterprise has frequently generated is received; concern is sent to the system mainte-',
 'nance including the observance of the law in recent years:',
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 'the',
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 'is received to manage customer and',
 '50',
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 'and the activ-',
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 '1td']

PFU Business report
No. 068
New customer's development
and increasing the sale of product
My country economy at this season keeps escaping from Odoba of business though holds a crude oil
high so on unstable element that continues still, and recovering gradually and well.
In the IT industry, there is an influence such as competing intensification in narrowing investment field.
(The main product and service at this season)
From the product headquarters
In the image business, the new model turning on of the A3 high-speed,
usually
bad
two sided color scanner that achieved a high-speed reading aimed at.
wroom was established in United States, Europe, and Asia/Oceania.
7%
20%
very good
Image business
good
47%
1) Scanner class
A3 high-speed, two sided color scanner "fi-5900C" that 100 high-n
26%
function to enable industry-leading was installed was announced in
ScanSnap gotten popular because of an office and individual use.
2) DLM solution scanner
The DLM solution that used received the rise of the concern to efficient
Satisfaction rating to new product
management and internal management of the corporate private circum-
stances report in recent years and attracted attention. The function of software that the inspection of data is possible by
the sense that turns over the file is strengthened, and easiness to use has been improved.
(approach on business risk)
In-house activity
The attestation intended for each office in Shinbashi, Kansai, and Tokai was acquired in environment ISO in February,
2006. In addition, it participates in the minus 6% that is a national movement of the global warming prevention, and
"Culbiz" is done. The scandal of the enterprise has frequently generated is received, concern is sent to the system mainte-
nance including the observance of the law in recent years.
100
Enhancement of system of management
90
The committee that aimed at the decrease of a variety of business
80
risks in an individual business talk was newly established. Moreover,
70
the recognition of "Privacy mark" is received to manage customer and
60
50
employee's individual information appropriately in 2001, and the activ-
40
ity based on the protection of individual information policy is continued.
30
It is ..bAsia/Oceania in globalln addition, our technology, commodity
20
power, and correspondence power were evaluating acquired.
10
0
1998 1999 2000 2001 2002 2003 2004
Satisfaction rating to new product
1
Business report Ltd

Recurrent models typically factor computation along the symbol positions of the input and output
sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden
states h,, as a function of the previous hidden state h;_, and the input for position t. This inherently
sequential nature precludes parallelization within training examples, which becomes critical at longer
sequence lengths, as memory constraints limit batching across examples. Recent work has achieved
significant improvements in computational efficiency through factorization tricks and conditional
computation [26], while also improving model performance in case of the latter. The fundamental
constraint of sequential computation, however, remains.

Attention mechanisms have become an integral part of compelling sequence modeling and transduc-
tion models in various tasks, allowing modeling of dependencies without regard to their distance in
the input or output sequences [2}|16]. In all but a few cases [22], however, such attention mechanisms
are used in conjunction with a recurrent network.

In this work we propose the Transformer, a model architecture eschewing recurrence and instead
relying entirely on an attention mechanism to draw global dependencies between input and output.
The Transformer allows for significantly more parallelization and can reach a new state of the art in
translation quality after being trained for as little as twelve hours on eight P100 GPUs.

2 Background

The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU
|20], ByteNet and ConvS2S [8], all of which use convolutional neural networks as basic building
block, computing hidden representations in parallel for all input and output positions. In these models,
the number of operations required to relate signals from two arbitrary input or output positions grows
in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes
it more difficult to learn dependencies between distant positions [11]. In the Transformer this is
reduced to a constant number of operations, albeit at the cost of reduced effective resolution due
to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as
described in section[3.2]

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions
of a single sequence in order to compute a representation of the sequence. Self-attention has been
used successfully in a variety of tasks including reading comprehension, abstractive summarization,
textual entailment and learning task-independent sentence representations [41/22 {23} [19].

End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-
aligned recurrence and have been shown to perform well on simple-language question answering and
language modeling tasks [28].

To the best of our knowledge, however, the Transformer is the first transduction model relying
entirely on self-attention to compute representations of its input and output without using sequence-
aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate
self-attention and discuss its advantages over models such as and [8].

3 Model Architecture

Most competitive neural sequence transduction models have an encoder-decoder structure [[5} 2} [29].

Here, the encoder maps an input sequence of symbol representations (71, ...,2,,) to a sequence

of continuous representations z = (21,...,2n). Given z, the decoder then generates an output

sequence (71, .--; Ym) Of symbols one element at a time. At each step the model is auto-regressive
, consuming the previously generated symbols as additional input when generating the next.

The Transformer follows this overall architecture using stacked self-attention and point-wise, fully
connected layers for both the encoder and decoder, shown in the left and right halves of Figure[I]
respectively.

3.1 Encoder and Decoder Stacks

Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two
sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-

Recurrent models typically factor computation along the symbol positions of the input and output
sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden
statesht, as a function of the previous hidden state ht1and the input for position t. This inherently
sequential nature precludes parallelization within training examples, which becomes critical at longer
sequence lengths, as memory constraints limit batching across examples. Recent work has achieved
significant improvements in computational efficiency through factorization tricks [ 18] and conditional
computation [ 26], while also improving model performance in case of the latter. The fundamental
constraint of sequential computation, however, remains.
Attention mechanisms have become an integral part of compelling sequence modeling and transduc-
tion models in various tasks, allowing modeling of dependencies without regard to their distance in
the input or output sequences [ 2,16]. In all but a few cases [ 22], however, such attention mechanisms
are used in conjunction with a recurrent network.
In this work we propose the Transformer, a model architecture eschewing recurrence and instead
relying entirely on an attention mechanism to draw global dependencies between input and output.
The Transformer allows for significantly more parallelization and can reach a new state of the art in
translation quality after being trained for as little as twelve hours on eight P100 GPUs.
2 Background
The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU
[20], ByteNet [ 15] and ConvS2S [ 8], all of which use convolutional neural networks as basic building
block, computing hidden representations in parallel for all input and output positions. In these models,
the number of operations required to relate signals from two arbitrary input or output positions grows
in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes
it more difficult to learn dependencies between distant positions [ 11]. In the Transformer this is
reduced to a constant number of operations, albeit at the cost of reduced effective resolution due
to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as
described in section 3.2.
Self-attention, sometimes called intra-attention is an attention mechanism relating different positions
of a single sequence in order to compute a representation of the sequence. Self-attention has been
used successfully in a variety of tasks including reading comprehension, abstractive summarization,
textual entailment and learning task-independent sentence representations [4, 22, 23, 19].
End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-
aligned recurrence and have been shown to perform well on simple-language question answering and
language modeling tasks [28].
To the best of our knowledge, however, the Transformer is the first transduction model relying
entirely on self-attention to compute representations of its input and output without using sequence-
aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate
self-attention and discuss its advantages over models such as [14, 15] and [8].
3 Model Architecture
Most competitive neural sequence transduction models have an encoder-decoder structure [ 5,2,29].
Here, the encoder maps an input sequence of symbol representations (x1;:::;x n)to a sequence
of continuous representations z= (z1;:::;z n). Given z, the decoder then generates an output
sequence (y1;:::;y m)of symbols one element at a time. At each step the model is auto-regressive
[9], consuming the previously generated symbols as additional input when generating the next.
The Transformer follows this overall architecture using stacked self-attention and point-wise, fully
connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,
respectively.
3.1 Encoder and Decoder Stacks
Encoder: The encoder is composed of a stack of N= 6 identical layers. Each layer has two
sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-
2