Leveraging Artificial Intelligence for Simplified Invoice Automation: Paddle OCR-based Text Extraction from Invoices

Authors : Jaya Krishna Manipatruni; R Gnana Sree; Ranjitha Padakanti; SreePriya Naroju; Bharani Kumar Depuru

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/4mxbdh36

DOI : https://doi.org/10.5281/zenodo.8409861

In this study, we delve into the utilization of PaddleOCR, a readily available tool for optical character recognition (OCR), in extracting text from invoices. It is of utmost importance to accurately extract data from invoices, including information about vendors, invoice dates, item descriptions, quantities and prices to effectively manage finances. We achieved this by leveraging the powerful deep learning models and pre- trained weights provided by PaddleOCR to process invoice images and extract the necessary textual details. Our investigation commences with a comprehensive analysis of the PaddleOCR framework, exploring its capabilities and potential for customization. We explore various techniques aimed at enhancing image quality and improving OCR accuracy. The PaddleOCR framework offers advanced functionalities such as text detection, recognition and layout analysis that we seamlessly incorporate into our workflow to accommodate diverse invoice layouts and formats.To train our OCR model effectively, we curate a meticulously crafted dataset comprising real world invoice images with varying characteristics. With this dataset in hand, we fine tune the PaddleOCR model with a specific focus on enhancing its performance in extracting text from invoices. Upon training the model successfully, we evaluate its performance using an independent test dataset while measuring key metrics like Character Error Rate (CER) and Word Error Rate (WER). Our research strongly confirms the efficacy of the PaddleOCR powered system in precisely extracting text from invoices that have different layouts and formats. Additionally, we conduct a comparison between our methodology and other OCR techniques, emphasizing the benefits of PaddleOCR's advanced deep learning framework.Furthermore, we seamlessly integrate the invoice text extraction pipeline into a comprehensive automated system for invoice processing. This integrated system streamlines the extraction, parsing, and organization of invoice data, leading to more efficient financial workflows. We also consider the potential applications of this technology, including invoice digitization, data analytics, and process automation, all of which contribute to significant improvements in operational efficiency and reduced manual labour in organizations. In summary, this research demonstrates the successful use of PaddleOCR for text extraction from invoices. Our developed pipeline excels in accuracy and adaptability across various invoice layouts, paving the way for increased automation in financial management and document processing.

Keywords : PaddleOCR, Optical Character Recognition (OCR), Data Extraction, Data Learning Models, Text Detection, Character Error Rate (CER), Word Error Rate (WER), Invoice Digitization, Data Analytics.


Paper Submission Last Date
31 - December - 2023

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