AI tries to help with a pdf to spreadsheet task.

This is a conversation I had with a couple of AI to help with converting a pdf file with email addresses in it for newsletter invitations. First ChatGPT then PI.ai

Using AI to help start a Newsletter (on AI topics)

In developing a newsletter, I’m faced with the task of creating a database of prospects to invite on board and a pipeline of initial description and invite through to final signup.

I have a database of past contacts as well as several other pdf’s of names, address, phone numbers and emails that I would like to set up for promotional offers.

I was wondering what AI could do to set this up for an email campaign of invites.

First I asked

you are experienced n tools that can read a pdf and extract tables and create spreadsheets and csv files from it. What 5 tools would you recommend with links to them?

ChatGPT answered

Extracting tables from PDFs and transforming them into more manageable formats like spreadsheets or CSV files is a common task, and a wide range of tools, including AI and machine learning-based ones, have been developed to handle it. Here are five tools that are known for being effective at this task:

1. **Tabula** — This is an open-source tool that allows you to extract that data into a CSV or Microsoft Excel spreadsheet using a simple, easy-to-use interface. You can find it here: [Tabula](http://tabula.technology/)

2. **PDFTables** — This is a commercial service that offers a simple API for PDF data extraction, it’s efficient and accurate for many use cases. They offer an online interface, but also an API that developers can use for automation. You can check it out here: [PDFTables](https://pdftables.com/)

3. **Apache PDFBox** — PDFBox is an open-source project run by Apache. It’s a Java library that comes with a variety of tools to work with PDF files. This includes a command-line utility that can extract tables and save them as CSV or other formats. It’s more technical and requires some programming knowledge, but it’s powerful and flexible. Here is the link: [Apache PDFBox](https://pdfbox.apache.org/)

4. **Amazon Textract** — Amazon Textract is a service that automatically extracts text, handwriting, and data from scanned documents. It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. This service is robust and can handle large volumes of data. It does require technical skills to implement. Here is the link: [Amazon Textract](https://aws.amazon.com/textract/)

5. **Able2Extract Professional** — This is a commercial software application from Investintech that provides the ability to convert, create, and edit PDF documents. It includes table extraction capabilities, allowing users to select and convert tables from PDFs into Excel. Here is the link: [Able2Extract Professional](https://www.investintech.com/prod_a2e_pro.htm)

Remember, each tool has its strengths and weaknesses, and the best one for your use case will depend on your specific needs and resources. For example, some tools may be better at handling complex tables, while others might offer more ease of use. Some of the commercial tools may provide more refined results, but open-source tools or services with APIs like Apache PDFBox and Amazon Textract offer more customization possibilities for developers.

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Can you describe a few pipelines using these tools?

Sure, here are a few examples of data pipelines that involve reading PDFs, extracting tables, and performing subsequent analysis. These pipelines will utilize a combination of different tools that are specialized for various stages of the pipeline.

1. **PDF Extraction and Sentiment Analysis Pipeline:**

- Step 1: Use a tool like **Tabula** or **PDFTables** to extract text from PDFs and convert tables into CSV format.

- Step 2: Clean and preprocess the data using a data manipulation library like **pandas** in Python.

- Step 3: Use the **Hugging Face Transformers** library to perform sentiment analysis on the text data using a pre-trained sentiment analysis model, such as BERT or DistilBERT.

2. **PDF Extraction and Text Classification Pipeline:**

- Step 1: Extract text data from PDFs using **Apache PDFBox**.

- Step 2: Clean and preprocess the text data, possibly using Python libraries like **pandas** and **NLTK** or **spaCy**.

- Step 3: Use **Hugging Face Transformers** to train a text classification model (like RoBERTa or ELECTRA) on the text data for a specific task (for example, classifying documents into different categories).

3. **PDF Extraction and Named Entity Recognition (NER) Pipeline:**

- Step 1: Use **Amazon Textract** to extract text from PDF documents.

- Step 2: Clean and preprocess the text using **pandas** or other data manipulation tools.

- Step 3: Apply a Named Entity Recognition (NER) model from **Hugging Face Transformers** to the text data to identify entities such as persons, organizations, or locations.

4. **PDF Extraction and Document Summarization Pipeline:**

- Step 1: Extract text from PDFs using a tool like **Able2Extract Professional**.

- Step 2: Clean and preprocess the text data.

- Step 3: Use a pre-trained summarization model from **Hugging Face Transformers** (like T5 or BART) to generate summaries of the documents.

5. **PDF Extraction and Language Translation Pipeline:**

- Step 1: Use a PDF extraction tool such as **PDFTables** to extract text from the PDF.

- Step 2: Preprocess and clean the extracted text using **pandas** or similar libraries.

- Step 3: Use a pre-trained translation model from **Hugging Face Transformers** (like mBART or T2T) to translate the text to a different language.

In all of these pipelines, the specific tools and models used can be swapped out depending on the exact requirements of the task. Also, more steps might be added for further data cleaning, validation, visualization, or other purposes. These are just simplified examples to give an idea of what a pipeline might look like.

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I then asked PI.ai, it said

For tools:

Absolutely! I’d recommend the following 5 tools for converting PDFs to CSV or spreadsheets:

1. Adobe Acrobat Pro — this is a paid tool, but it’s the most well-known and widely used PDF converter. It’s also very accurate and preserves the formatting and layout of the original PDF.

2. ProperSoft PDF2CSV — this is a more affordable option that still offers good quality conversions. It’s also easy to use, and can handle large and complex PDFs.

3. iSkysoft PDF Editor Pro. It’s a good middle ground between simplicity and accuracy.

It’s easy to use, but still offers a lot of features. It also has a nice user interface. And it’s quite affordable, too!

4. Simpo PDF to Excel — this tool is very affordable, and is known for its speed and ease of use. It’s great for converting basic PDFs to spreadsheets, but may not be as accurate with more complex PDFs.

5. Able2Extract — this tool is known for its ability to accurately convert even the most complex PDFs into CSV or spreadsheet files. It also has a good user interface and is relatively affordable. I hope this helps!

For pipelines:

Sure! I can give you some examples of pipelines using these tools.

One option is to use a tool like Adobe Acrobat Pro to convert the PDF to a text file, and then use a tool like ProperSoft PDF2CSV or iSkysoft PDF Editor Pro to convert the text file to a CSV file.

Another option is to use a tool like Apache Airflow, which can extract data from a PDF file and save it to a CSV file.

There are also cloud-based solutions like Amazon Textract that can automatically extract data from PDF files and convert it to a CSV file.

I can give you 3 new pipelines that use a combination of tools like Python, Pandas, and BeautifulSoup.

First, we could use a tool like Python to extract data from a PDF file and save it to a text file. Then, we could use the Pandas library to read the text file and create a dataframe.

Finally, we could use a tool like BeautifulSoup to parse the dataframe and create a CSV file.

The next pipeline could be built with a similar combination of tools, but we could incorporate a tool like Tabula, which is specifically designed for extracting data from PDF files.

We could also use a tool like OpenRefine to clean and organize the data before we save it as a CSV file.

Lastly, we could use a tool like R to create charts and visualizations based on the CSV file.

Got another one?

This time, let’s focus on building a pipeline that’s entirely cloud-based.

We could use Amazon Web Services (AWS) to extract and convert data from PDF files.

AWS has a suite of tools called AWS Lambda, which can automatically extract data from PDFs and convert it to a CSV file.

Then, we could use AWS Glue to clean and organize the data, and finally save it to a CSV file. Does that sound like something you’re interested in?

(wait, what? Lambda is serverless service , maybe it meant something else?)

Is that what lambda does? I thought it was a serverless resource. Do you mean a different AWS set of tools?

That’s a great observation! You’re right that AWS Lambda is a serverless computing service, and it can’t be used to analyze data directly. But you’re also right that there are other tools in the AWS ecosystem that can be used for data analysis.

For example, we could use AWS Redshift, which is a data warehouse service, to load, query, and analyze our CSV file. We could also use AWS Athena, which is a serverless interactive query service that we could use to analyze the data in our CSV file.

Now I’m wondering what other AI might say