Challenge

Traditional invoice processing is a time-consuming and error-prone process. It involves manual data extraction from invoices and verifying them against different sources before entering into internal systems. If a data entry operator makes a mistake, it can lead to incorrect payments, delayed payments, or even loss of revenue. Another challenge of traditional invoice processing is that it usually requires a significant resource which leads businesses to hire additional staff to process invoices.

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Solution

The primary goal was to develop a solution using Optical Character Recognition (OCR) technology to extract invoice amounts accurately and efficiently from images of invoices. This would enable automated processing of invoices and facilitate smoother financial transactions.

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OCR Integration

The team integrated OCR functionality into their existing Python- based system. OCR technology was chosen for its ability to recognize text within images and convert it into machine-readable data.

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Image Processing Pipeline

Images of invoices were fed into the system’s image processing pipeline, where OCR algorithms analyzed the text content. The OCR engine was trained to identify and extract relevant information, such as invoice amounts, based on predefined criteria.

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Keyword Extraction

Using OCR, the system identified keywords such as “invoice amount” and associated values within the text of the invoices. Advanced text processing techniques were employed to accurately locate and extract numerical values corresponding to invoice amounts

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Data Validation

Extracted invoice amounts were cross-referenced with associated customer IDs or invoice IDs to ensure accuracy and integrity of the data. Any discrepancies or errors were flagged for manual review and validation.