The document discusses PDF optical character recognition (OCR) which uses neural networks like convolutional neural networks and long short-term memories to convert scanned and handwritten PDF text into machine-encoded text. It describes how modern OCR tools use techniques like denoising with generative adversarial networks and document identification with siamese networks during pre-processing. Applications of PDF OCR include extracting numerical data for analysis and interpreting text data using natural language processing.

1. PDF OCR
Overview

2. 2
Introduction
PDF Optical Character Recognition (OCR) is
the process of converting PDFs of scanned
and handwritten text into machine-encoded
text such that it could be further used by
programs for processing and analysis.

3. 3
Advances in PDF OCR Solutions
Modern OCRs use Neural Networks that mimic the way
human brains learn. In the case of Deep-learning based
OCRs, 2 genre of neural networks are applied.
Convolutional Neural Networks (CNNs): CNNs are one of
the most dominant sets of networks used today particularly
in the realm of computer vision. It comprises multiple
convolutional kernels that slide through the image to
extract features.
Long Short-Term Memories (LSTMs): LSTMs are a family
of networks applied majorly to sequence inputs. The
intuition is simple -- for any sequential data (i.e., weather,
stocks), new results may be heavily dependent on previous
results, and thus it would be beneficial to constantly feed-
forward previous results as part of the input features in
performing new predictions.

4. 4
Pre-processing in PDF OCRs
Besides the main tasks in OCR that incorporate deep learning, many pre-processing stages to
eliminate rule-based approaches are deployed.
Denoising: A recent approach adopted by OCR technologies is to apply a Generative
Adversarial Network (GAN) to “denoise” the input. GAN is trained from a pair of denoised and
noised documents, and the goal for the generator is to generate a de-noised document as
close to the ground-truth as possible.
Document Identification: Knowing the type of document the OCR machine is currently
processing may significantly increase the accuracy of data extraction. Recent arts have
incorporated a Siamese network, or a comparison network, to compare the documents with
pre-existing document formats, allowing the OCR engine to perform a document classification
beforehand.

5. 5
Applications of PDF OCRs
The main goal of a PDF OCR is to retrieve data from unstructured formats, whether that be
numerical figures or text.
Numerical Data Analysis: When PDFs contain numerical data, OCR helps extract them to
perform statistical analysis. Specifically, OCR with the help of table or key-value pairs (KVPs)
extractions can be applied to find meaningful numbers from different regions of one given
text.
Text Data Interpretation: Text data processing may require more stages of computation, with
the ultimate goal for programs to understand the “meanings” behind words. Such a process of
interpreting text data into its semantic meanings is referred to as Natural Language
Processing (NLP).

6. 6
PDF OCR - Nanonets™ Advantage
Nanonets™ PDF OCR uses deep learning and therefore is completely template and rule
independent. Not only can Nanonets work on specific types of PDFs, it could also be applied
onto any document type for text retrieval.
Post-processing: On Nanonets™, you can post-process your data after extraction. For
example, if there are any errors on the extracted data, you can write some scripts to clean the
extracted data and export into desired format.
Fraud Checks: If there’s any financial or confidential data in our documents, Nanonets™
models can also perform fraud checks.
High Accuracy: Provides high data extraction accuracy of 95%+. The model also employs
state of the art AI that improves with every document it extracts.

7. 7
Learn more about
PDF OCRs:
https://nanonets.com/blog/pdf-ocr/