Stuck with Document Barcode Splitting? It’s Time for an AI-Powered Upgrade!
Imagine if a document could write a travel blog—it would have some incredible stories to share. From desk to desk, system to system, it moves through countless workflow stages, getting reviewed, revised, and referenced repeatedly.
It might be an adventurous trip for your document, but not for you. Whether it’s a financial report, a compliance document, or an invoice, finding the right information from a huge pile of files when you need it can be frustrating and time-consuming.
Manually searching through pages isn't just inefficient—it slows everything down. For decades, businesses have sought ways to simplify document processing, and document barcode splitting was one of the earliest automation methods.
Barcodes, Barcodes Everywhere - But None on a Document!
Barcodes are the most widely used identification technology, playing a crucial role in retail, logistics, and document management. The global barcode reader market was valued at $3.2 billion in 2022 and is expected to grow at a CAGR of 7.5% from 2023 to 2028.
Each day, over 5 billion barcodes are scanned worldwide. Barcode statistics reveal that more than 2 million companies, primarily in the retail sector, rely on GS1-standardized barcodes for their daily operations.
You must have seen cashiers scanning barcodes at grocery stores to retrieve product details instantly. These barcodes contain essential data, including product numbers, inventory details, and pricing information.
Similarly, barcodes on delivery packages provide key details about shipments, ensuring efficient logistics, real-time tracking, and optimized inventory control. This technology has transformed transportation and supply chain management, reducing errors and improving speed.
However, have you come across barcodes on documents recently? Chances are, you haven't. Keep reading to know why.
What’s Document Barcode Splitting and How Does it Work?
Document barcodes are specific machine-readable codes present in documents that act as markers within the document. They are primarily used for document splitting, identifying different documents within the same file, or separating pages or sections within a single document - this is called document barcode splitting. The following is a simplified workflow of how document barcode splitting works.
Are Document Barcode Splitting Still Relevant Today?
The answer is a straight no. With the rise of AI-powered OCR document processing and Intelligent Document Processing (IDP), traditional barcode splitting is quickly becoming outdated and less effective.
Relying on barcode-splitting technology for document classification is like running Windows XP on an 11th Gen computer—outdated, inefficient, and nowhere near optimized for today’s data-driven workflows.
Want to automate your entire document process, from classification to data extraction? Talk to our AI experts and discover the perfect automation solution tailored to your business.
Advanced AI ML OCR vs. Traditional Document Barcode Splitting for Document Classification
AI-powered OCR does more than classify documents—it reads and processes entire files with precision. With modern technology, document splitting is no longer a hurdle. Advanced systems extract data without relying on barcodes, making barcode splitting an outdated and unnecessary step. If a vendor is selling it as a standalone solution, they’re charging for a process that AI has already replaced.
Document Barcode Splitting vs. AI-ML OCR Classification
- Dependency on Barcodes
Barcode document OCR splitting relies entirely on the presence of barcodes in documents to split or classify pages. This requires barcodes to be placed accurately in designated locations for the system to properly classify the different documents. While this approach can work for structured documents with predefined templates, it is increasingly impractical as over 70% of documents are unstructured. This problem becomes even more challenging when classifying documents from various timeframes and regions.
Additionally, barcodes do not magically appear on documents with vital information encoded. The document information is extracted from ERP systems and document management workflows and then encoded into the barcode. This adds an extra step, consuming more time and resources, whether done in-house or outsourced.
On the other hand, advanced ML OCR solutions or Intelligent Document Processing softwares eliminate the need for barcodes entirely. Modern OCR systems can analyze the text within a document in its context, enabling seamless classification.
- Data Extraction
Document barcodes contain limited information, such as document type, document number, creation date, and page number. However, extracting specific information from the document is not possible with barcodes alone. For that, you would need to do it manually or rely on additional tools.
In contrast, ML OCR/IDP systems can not only read but also understand text within the document's context. This allows easy extraction of any information from the documents.
- Template Sensitivity
Barcodes on documents must be placed in specific areas to be accurately scanned. Traditional OCR systems require barcodes to follow certain templated formats for successful scanning. This makes barcode placement and identification a major challenge, especially for complex documents—which account for over 70% of documents globally—and can lead to errors in the process.
Modern OCR systems, however, are trained to extract information regardless of a document's template, making them far more reliable for complex and unstructured documents.
- Workflow Scalability
Using document barcode splitting is just one step in the data extraction process. Extracting specific information from documents often requires manual effort or additional rounds of document processing. This results in multiple workflows, limiting scalability.
ML OCR systems, on the other hand, combine classification and data extraction into a single workflow. This enables you to scale your workflows, process documents faster, and achieve the highest levels of accuracy.
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In a Nutshell
The barcode industry is thriving, with its net worth expected to hit $14.4 billion by 2030. However, there is a reverse trend regarding document barcodes, which are primarily used for splitting and classification. The best alternative on the market is to deploy AI-ML-powered OCR solutions that not only classify documents but also extract data from even the most complex, semi-structured documents. This makes your workflows simpler, faster, and more efficient.
FAQs
A pre-fund QC checklist is helpful because it ensures that a mortgage loan meets all regulatory and internal requirements before funding. Catching errors, inconsistencies, or compliance issues early reduces the risk of loan defects, fraud, and potential legal problems. This proactive approach enhances loan quality, minimizes costly delays, and improves investor confidence.
A pre-fund QC checklist is a set of guidelines and criteria used to review and verify the accuracy, compliance, and completeness of a mortgage loan before funds are disbursed. It ensures that the loan meets regulatory requirements and internal standards, reducing the risk of errors and fraud.
Using AI for pre-fund QC audits offers the advantage of quickly verifying that loans meet all regulatory and internal guidelines without any errors. AI enhances accuracy, reduces the risk of errors or fraud, reduces the audit time by half, and streamlines the review process, ensuring compliance before disbursing funds.
Choose software that offers advanced automation technology for efficient audits, strong compliance features, customizable audit trails, and real-time reporting. Ensure it integrates well with your existing systems and offers scalability, reliable customer support, and positive user reviews.
Audit Quality Control (QC) is crucial for mortgage companies to ensure regulatory compliance, reduce risks, and maintain investor confidence. It helps identify and correct errors, fraud, or discrepancies, preventing legal issues and defaults. QC also boosts operational efficiency by uncovering inefficiencies and enhancing overall loan quality.
Mortgage review/audit QC software is a collective term for tools designed to automate and streamline the process of evaluating loans. It helps financial institutions assess the quality, compliance, and risk of loans by analyzing loan data, documents, and borrower information. This software ensures that loans meet regulatory standards, reduces the risk of errors, and speeds up the review process, making it more efficient and accurate.
