Invoiveocr: deep learning based automatic invoice recognition process

In recent years, the automatic recognition of invoices has emerged as a critical component in document digitization, particularly within the financial sector, where efficient data extraction from structured documents is essential. This paper introduces an advanced method for automating invoice recognition by combining cutting-edge image preprocessing, OCR pipeline techniques, and post-processing strategies. The process begins with a robust image preprocessing stage that includes scaling, binarization, noise reduction, and skew correction based on Hough transform; learning-based angle regressors may offer adaptive alternatives for noisy or multi-aligned invoices, all of which enhance the quality of the input images, ensuring optimal performance during OCR. The core of the system is an OCR pipeline that integrates state-of-the-art text detection and recognition algorithms, effectively handling various challenges such as multi-oriented and densely packed text regions. After the initial recognition, a series of post-processing steps, including error correction, field validation, and structured data formatting, are applied to ensure that the extracted text is accurate and conforms to predefined invoice formats. Extensive experiments conducted on a dataset comprising 6349 images, including both electronic and paper invoices, demonstrate that the proposed approach achieves high recognition accuracy and reliable performance. These results highlight the system’s effectiveness in automating invoice recognition, confirming its applicability in real-world financial applications where precision and efficiency are paramount. The method provides a comprehensive solution for improving invoice digitization and streamlining financial workflows.