How to Export from PDF to Google Sheets in Three Different Ways

This is not only time-consuming, but also prone to human error, so automating it would be preferable.

Now that we've established that converting PDFs to Google sheets is necessary, let's look at the structure of PDFs and the difficulties inherent in doing so.

Do you need to change PDFs into Google Docs spreadsheets? The free PDF to CSV converter by Nanonets is worth your time. Or, read on to learn how Nanonets can streamline your entire PDF-to-Google-Sheets process.

Nanonets-automated data-conversion processes

Difficulties in Reading and Understanding PDF Files

Adobe was the company that created the portable document format (PDF), and then released it to the public as a standard. Since it is OS-neutral, it has seen widespread adoption.

Why, then, is it so difficult to extract information from a PDF and re-present it in a different format? The following visuals will demonstrate the point effectively.

Snapshot of a Basic PDF File

The above snapshot is a screen capture of a PDF file being viewed in a PDF reader. Let's switch gears and use a text editor to read the same PDF file.

The PDF in a text editor

You can tell from the images above that the original data structure is completely destroyed when information is saved as a PDF. This is due to the fact that all a PDF file is, essentially, are directions on how to print or draw a string of characters.

Data extraction from tables is even more complicated than text extraction because of the wide variety of table formats in use.

I think you can see now that turning a PDF into a Google Sheets form is not exactly a stroll in the park. The next section discusses how the majority of up-to-date PDF parsers go about figuring out what's in a PDF document and extracting that data.

PDF Document Parsing with Today's Technology

To extract structured information from PDF files, most up-to-date parsers follow the procedure outlined here.

A typical workflow for modern PDF parsers, depicted in a flowchart

Let’s take a quick look at each stage:

1: Preprocessing, also known as Data Cleaning:

If your PDF is well-presented, your Machine Learning model will have an easier time gleaning information from it. Scanned PDF documents, for instance, will have scan artifacts that could slow down the conversion process.

Common preprocessing techniques include binarization, skew correction, and appropriate filtering to remove noise. Excellent case studies of document preprocessing prior to Optical Character Recognition (OCR) processing can be found in the following Nanonets post, Nanonets Tesseract Post.

The majority of the action takes place here. A Machine Learning (ML) model is typically used for data extraction. The majority of machine learning models used for PDF data extraction use a suite of optical character recognition, text and pattern recognition, etc.

For the purposes of this post, the model can be thought of as a black box that consumes your PDF and spits out the parsed data. In addition, it is re-trainable with company-specific data because ML is at its heart.

Third, in Post-Production,

Data is extracted and then transformed into the desired format (CSV, XML, JSON, etc.). Further, the AI-generated predictions are supplemented by user-defined rules. For example, rules for the output's format, additional restrictions on the extracted information, etc.

Some potential metrics for evaluating a PDF parser's effectiveness are discussed below.

You need PDF to Google Docs or PDF to Google Sheets conversion, right? Learn how Nanonets can streamline your entire process of converting PDF files to Google Sheets.

Extracting tables automatically using Nanonets

KPIs for PDF Document Conversion

The accuracy and speed of table extraction from a PDF document is a critical factor in judging the performance of the PDF converter because most PDF converters will be used for invoice processing or related tasks.

2. Fluency in multiple languages:

Invoices are a common source of confusion for many large businesses because they can come in any language. Either multilingual parsing should be an out-of-the-box feature of the PDF parser, or users should be able to train the model with their own data.

Thirdly, Compatibility with Common Accounting Packages

The ideal PDF converter will be a simple add-on to your current document processing system. Typical accounting packages like QuickBooks, Xero, Wave, etc., should be compatible with it.

Simple and Intuitive to Use:

Users with little technical knowledge will likely handle the tool's controls. It would be useful if it could be used with low levels of technical expertise.

Techniques for Migrating from Portable Document Format to Google Spreadsheets

First, we'll go over how to change a PDF into a Google Sheet in Google Docs.

Tables and text in simple PDF files can be automatically recognized by Google Drive. You need only:

1. Save your PDF to Google Drive.

2. Just pick "Open with Google Docs"

3. To use the information in Google Sheets, simply copy and paste.

Even though it appears to be effective, let's try something more realistic. Examine this basic invoice for clarification.

When I open this file in the Google Docs app, I get:

It is evident that as the document's complexity rises, we will require increasingly sophisticated means of data recognition.

PDF tables extractor, Online2PDF, and other similar tools are just a few of the many online resources that offer built-in support for transforming PDFs into Google Sheets through seamless integration with Google Drive.

When these programs were put to the test using the sample invoice PDF provided up top, however, they failed to identify the tables in the vast majority of instances.

Need to transform PDFs into Google Sheets If you need to convert PDF files to CSV format, try out Nanonets's free tool. Examine the examples provided below to learn how to use Nanonets to automate your entire PDF to Google Sheets workflow.

Extraction of tables using Nanonets for automated processing

Converting Documents to Google Sheets Automatically

In order to fully automate the process of parsing the PDF and extracting the data into a Google Sheets form, we will be using the following tools.

With Webhooks, First, You Can:

An example of a webhook is an HTTP request with a specific definition. In most cases, they are activated in response to event i. e The program will notify a specified website when a certain event occurs.

Just how can you implement this into your current system to streamline processes? Take the common scenario of invoicing as an example. You have a PDF to Google Sheets converter in the cloud and use it to import your supplier invoices. Is there a way to tell when the model has completed its work on the files?

Utilizing a webhook that alerts you when the PDF data has been converted to a Google Sheets document saves you the trouble of having to repeatedly check for completion.

To make use of application programming interfaces

Interface for Programming Applications, or API for short. PDF files can be imported into Google Sheets with a few simple lines of code if the right API calls are made.

Put the PDF files into the Google Sheets converter. NanonetsAPI; success_code; unique_id = uploaddata(PDF_documents)

When your PDF documents have been successfully converted, you will receive a notification if your company has set up the integration with Webhooks. When you're ready, use the code below to access Google's Sheets API and download the form.

Google Sheets form templates are available for download. NanonetsAPI = Google_sheets_data downloaddata(unqiue_id)

Convert PDF files to Google Sheets using Nanonets

With Nanonets' PDF parser, you can reliably and easily perform these tasks. Furthermore, nanonets can be used to extract information from emails and import it into Google Sheets.

An example invoice was processed by the PDF parser. In this part, we see how the tool performs and how simple it is to operate. The following pictures do a better job of demonstrating how awesome it is than words ever could.

The sample invoice used to test Nanonets' PDF parser is displayed in the following screenshot.

This is a sample PDF for use with Nanonets' PDF reader.

To submit the invoice, just go to Nanonets' website. It only takes a few seconds to convert, and then you can download the parsed data in a variety of formats like CSV, XLSX, etc. (Use Nanonets's PDF to CSV Convertor)

Resulting PDF screenshot

Next is a snapshot of the CSV file that contains the extracted data from the PDF.

CSV file

Finally, you can import the XLSX/CSV file into Google Drive to transform the CSV file into a google sheets form. Using Google drive APIs, this process can be automated.

Information imported from a.csv file into Google Sheets

This section demonstrates the use of the Nanonets PDF parser to construct a basic pipeline.

Do you need to gather data from PDFs and incorporate it into a Google Sheets document? If you want to quickly and easily import data from PDFs into Google Sheets, check out NanonetsTM.

Making a Basic Flowchart

First, use the Nanonets Application Programming Interface to upload PDF files automatically.

Use the Nanonets Application Programming Interface to upload your parsing documents automatically. You can see how this is accomplished in Python with the code below:

You can use this API to import PDFs into the Nanonets simulation.

2. Integrate webhooks to be notified when parsing is complete.

Once the documents have been parsed, you can set up a webhook to notify you immediately.

3. Evaluate, and then transfer to Google Sheets

Check the downloaded CSV files for accuracy before uploading them to Google Sheets with the help of the Google drive API.

An Advantage of Nanonets

The Nanonets PDF Parser is the best tool for your company because it has these features.

First, External Integrations:

MySql, Quickbooks, Salesforce, etc., can all be easily incorporated into the Nanonets model. As a result, the nanonets converter can be easily integrated into your existing setup without requiring any changes.

Fast processing with a high degree of accuracy:

When compared to similar tools, Nanonets' PDF parser has a much higher rate of accuracy (over 95%).

Awesome Tools for Post-Production:

Assume that the nanonets model has been incorporated into your database. Based on the information extracted from the document, the model will automatically complete some fields (using data from your database). Such as:

Afterwards, Nanonets can take advantage of some very useful features.

A database lookup is used to automatically populate the Registered_ID field with the Invoice_ID that was extracted from the PDF, as depicted in the figure.

Four. An Easy-to-Use Design

The feature is underappreciated, but the user interface and user experience are excellent. The time required to register, upload the document, and process the data was less than 5 minutes. That's about as long as it takes for my computer to boot up.

A Massive Number of Customers

If you're still on the fence about whether or not to adopt Nanonets for workflow automation, you might want to consider the following organizations as case studies.

• Deloitte
• The Sherwin Williams Company
• DoorDash
• P&G

Do you need to gather data from PDFs and incorporate it into a Google Sheets spreadsheet? Using NanonetsTM, you can quickly and easily import data from any PDF into Google Sheets.

Conclusion

In this article, we explored how a PDF to Google Sheets converter can streamline your operations.

Sending information from Gmail to Sheets or from Outlook to Excel is another use for Nanonet.

First, we discovered why it's important to make the switch from PDF to Google Sheets, and then we discovered the difficulties that arise when making this transition. We then dove into how modern PDF parsers tackle this task, and even put some of the more common solutions into practice.

Moreover, we discovered how to fully automate the process by leveraging third-party integrations like webhooks and APIs. Finally, we used the Nanonets tool to extract data from a sample invoice and explore some of its neat post-processing features in Google Sheets.

When it comes to modeling, have you tried the Nanonets approach? If so, please share your thoughts on the tool after using it. But if you don't believe me, give it a shot. That's all it takes to brighten your day