3 posts tagged with "excelifier"

Introduction​

Manual data entry is a tedious and error-prone task that most of us have to deal with frequently. It consumes valuable time and can lead to mistakes that affect financial records. Excelifier can help dealing with this necessary evil by allowing for easy importation into bookkeeping systems such as Fennoa. Let's explore how to use Excelifier to automate the insertion of purchase invoices, minimizing manual intervention and maximizing accuracy.

Proof of Concept​

We built excelifier-fennoa to assist in inserting purchase invoices into our bookkeeping system. By releasing it as a proof of concept, we aim to demonstrate how Excelifier can efficiently automate and integrate into financial workflows. This same method could be expanded to other bookkeeping systems, showing the versatility and adaptability of our solution.

How It Works​

The process is straightforward: Excelifier converts PDF invoices into structured JSON data, which is then formatted to interface with the Fennoa API. The invoices are automatically pushed into the bookkeeping system. Once the invoices have been uploaded, it is advisable to verify the content and accept the invoice for further processing. This automation not only saves time but also significantly reduces the risk of errors.

The tool is designed to handle multiple invoices simultaneously, enhancing productivity and streamlining bookkeeping operations.

Source code​

The source code for the project is available on GitHub.

1. Clone the repository: Download the source code to your local machine and add your Fennoa credentials to the .env file.
2. Prepare Your Documents: Place your original invoice PDF and the JSON file generated by Excelifier into the data_in folder.
3. Start the Application: Execute the command yarn start in your terminal. This process reads the files, extracts the data from the JSON, and inserts it along with the PDF into Fennoa.

Feel free to explore and adapt it to your needs. We welcome your feedback and suggestions for further development.

Conclusion​

Excelifier can significantly streamline the process of importing invoices into your bookkeeping system. By automating the conversion of PDFs to structured data, you can eliminate manual data entry and reduce the risk of errors. This proof of concept demonstrates the potential of Excelifier to enhance financial workflows and improve efficiency.

We are planning to expand the capabilities of this proof of concept and would love to hear your feedback. Let us know what you think!

We built a handy tool that processes multiple documents at once, aiming to simplify your workflow when dealing with bulk PDF conversions.

Overview​

At its heart, this tool revolves around a simple but effective JavaScript setup, consisting of two main folders: data_in for input PDFs and data_out for the converted JSON files. The process is straightforward: drop your files into data_in, and the tool automatically processes and delivers them as structured data in data_out.

How It Works​

Upon placing files into data_in, the tool kicks into gear, sending them to Excelifier for conversion. Each file gets its unique ID, with a corresponding subdirectory created under data_in. This acts like a mini-status board, indicating which files are in the process.

In the meantime, a separate part of the script monitors these subdirectories. Once it identifies that a file's conversion is complete, it pulls the converted JSON content into data_out and cleans up by removing the temporary subdirectory. This seamless operation ensures a tidy work process, focusing only on the end results: your converted files.

Performance​

We put it to the test by dropping 10 one-page invoices into data_in, and in a mere 1 minute and 15 seconds, all files were processed and waiting in data_out. Speed and efficiency are clearly at the forefront of this tool's design.

Availability​

Here's possibly the best part: The code is freely available in Github.

Please note: You will need Excelifier API keys to use it.

Why Use Our Tool?​

Aside from the clear time-saving advantages, this tool offers a streamlined method for managing and converting documents. It is particularly valuable for businesses or individuals who often handle a multitude of PDFs, providing a dependable solution to automate the conversion process with minimal fuss.

This compact JavaScript-based tool serves as a powerful demonstration of Excelifier's backend capabilities. It also offers a quick way to evaluate our efficiency!

This is just one of numerous ways Excelifier can enhance document processing workflows.

For more insightful ideas and updates, follow our blog!

Introduction to Excelifier​

In the realm of data extraction from PDF documents, numerous tools vie for a developer's attention. However, Excelifier sets itself apart through a combination of innovative approaches and developer-centric features. Let’s dissect each of these distinguishing aspects to understand what makes Excelifier not just another tool in the market.

Preconfigured Schemas by Document Type​

While most tools require manual setup for each document type, Excelifier streamlines this process significantly. Our preconfigured schemas are designed to understand the common structure of various document types, such as invoices, forms, and reports. This feature saves developers considerable time and effort, allowing them to bypass the meticulous process of defining rules for data extraction for each new document format they encounter.

• Technical Insight: The schemas are developed using a combination of OCR and machine learning to recognize and categorize different sections and data types within a document, from text blocks to tables and images, thus automating the extraction process with high accuracy.

No Separate Definitions Required for Each Document​

The norm in document data extraction often involves defining custom extraction rules for every single document, even within the same document type. Excelifier’s architecture uniquely negates this requirement by utilizing intelligent algorithms that adapt to variations within the same document category.

• Technical Insight: Leveraging LLMs, Excelifier can understand the context and variations in document layouts, making it robust against changes and discrepancies in formats, thus maintaining consistent data extraction without constant recalibration.

Support for Fine-tuning with Task-Specific Instructions​

Acknowledging that no solution can be one-size-fits-all, Excelifier offers flexibility through the capability for fine-tuning. Developers can apply task-specific instructions to address unique requirements or to improve extraction accuracy for specific datasets.

• Technical Insight: This is facilitated through a simple yet powerful instruction interface, where developers can input commands or parameters that adjust the tool’s behavior. Whether it’s dealing with peculiar document layouts or extracting data that requires special attention, this feature ensures developers maintain control over the process.

Fast Processing Time​

Speed is of the essence in data processing, and Excelifier excels in this aspect as well. With an average processing time of approximately 30 seconds per document, Excelifier stands out in rapid data extraction, ensuring that workflows are not bottlenecked by the document conversion process.

• Technical Insight: This efficiency is achieved through optimized OCR algorithms and the streamlined processing power of LLMs, both of which work in tandem to minimize processing time while maximizing accuracy and reliability of the extracted data.

In conclusion, Excelifier not only promises a solution to the common pain points in PDF document data extraction but also delivers a suite of features thoughtfully designed with the developer in mind. By reducing setup time, offering adaptability, enabling precise control with fine-tuning, and ensuring quick processing, Excelifier positions itself as a critical tool for developers aiming to enhance their data extraction workflows.