{"id":26039,"date":"2025-01-28T14:08:08","date_gmt":"2025-01-28T14:08:08","guid":{"rendered":"https:\/\/parashift.io\/?p=26039"},"modified":"2025-11-30T22:46:46","modified_gmt":"2025-11-30T22:46:46","slug":"scanning-service-provider-optimizes-document-digitization-through-automatic-document-separation","status":"publish","type":"post","link":"https:\/\/parashift.ai\/en\/scanning-service-provider-optimizes-document-digitization-through-automatic-document-separation\/","title":{"rendered":"Scanning service provider optimizes document digitization through automatic document separation"},"content":{"rendered":"\n

Leading Swiss BPO significantly increases efficiency in document capture thanks to AI-based document separation.<\/em><\/em><\/p>\n\n\n\n

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Overview \u2013 Management Summary<\/h3>\n\n\n\n
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By implementing automatic document separation with Parashift<\/a>, a major specialist IT service provider in the Swiss market can significantly optimize document digitization.<\/p>\n\n\n\n

The service provider is very familiar with the challenges involved in this process step and the associated optimization potential.<\/p>\n\n\n\n

With the newly designed process based on AI procedures, the goals set were achieved. Automated document separation with Parashift saves costs and processing time.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Challenges<\/h3>\n\n\n\n

Every day, the specialist IT service provider processes thousands of documents for its customers. In this case, the documents enter the capture process in paper form. However, incoming digital documents also benefit from this functionality. The documents practically always enter the process in larger quantities (batches\/files) and have to be separated.<\/p>\n\n\n\n

In this case, incoming mail was separated manually by people on the screen after scanning in document capture software; in many other cases, documents are separated during scanning using barcodes affixed during document preparation (AVOR) or inserted barcode or patchcode separator sheets.<\/p>\n\n\n\n

Both variants require a significant amount of manual work. In addition, incorrect handling can result in corrupt documents, and currently available tools for automatic separation have hardly become established due to their lack of reliability.<\/p>\n\n\n\n

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The solution<\/h3>\n\n\n\n
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Automated document separation was implemented on the basis of GNNs (Graph Neural Networks), a powerful artificial intelligence tool for analyzing and predicting relationships between objects.<\/p>\n\n\n\n