Just released: 2024 Manufacturing and Distribution Benchmark Report reveals how 900 companies increase CSAT and profitability

Blog Post

The Advantages, Challenges, and Alternatives to OCR Solutions

Business information trapped on paper continues to throttle productivity and open the door to manual handling errors. While OCR Solutions initially promised to eliminate the drudgery of data entry, in practice, it falls short of meaningfully extracting information from complex documents.

A better path forward is offered by modern AI-powered document processing. AI delivers unprecedented accuracy by understanding the language and context of documents instead of just matching character patterns.

Read on as we discuss the capabilities and pitfalls of OCR Solutions, and explore smarter data strategies that drive growth.

What Are OCR solutions?

OCR (Optical Character Recognition) solutions are applications designed to eliminate tedious manual data entry by detecting text in scanned documents or images and converting it into an editable and searchable digital format. OCR acts as an automated data entry clerk, sparing your team from transcribing information and giving you rapid access to business-critical data trapped in paper documents.

Specifically, OCR solutions are used to:

  • Eliminate the need for manual data entry
  • Process business orders, invoices, receipts, and shipping notices digitally
  • Improve efficiency, accuracy, and data accessibility for businesses and organizations

 

How Do OCR Solutions Work?

OCR solutions use computer vision and machine learning algorithms to analyze scanned documents or images, recognize text, and convert it into machine-readable and editable formats. The OCR process typically involves pre-processing the image, identifying and extracting text regions, and then using pattern recognition to convert the text into a digital format.

Here is how OCR technology functions in 4 key steps:

1. Document Digitization

The initial step involves converting paper invoices, sales orders, or other documents into digital format via scanning or photographing. Modern OCR (Optical Character Recognition) technology can connect with scanners to simplify this process. The scanning phase captures all elements of the document, such as text and diagrams, creating digital images.

2. Text and Data Extraction

The OCR solution analyzes the scans using recognition algorithms to identify characters and other semantic data. There are two main technical approaches:

  • Pattern Recognition: Relies on comparing scanned text to libraries of font templates. By matching scanned letters to these reference models, it can accurately recognize printed and typed text. However, foreign characters, stylized fonts, and handwriting can sometimes confuse OCR.
  • Feature Detection: Also known as Intelligent Character Recognition (ICR), this approach looks at strokes, loops, line spacing, and other attributes to detect letters and words instead of outright template matching. This can handle documents with multiple fonts, form labels, handwriting, and other hard-to-recognize elements.

3. Text Structuring and Export

Once the characters and semantic data are identified, the OCR solution transforms the extracted information into a digital format, such as a Word document, PDF file, or spreadsheet.

4. Searchable, Editable Digital Files

The OCR-processed text is suitable for searching and editing with any compatible software, unlocking the data trapped within physical sales orders, invoices, or other documents, and allowing your business to repurpose, analyze, and manage the information digitally.

What Are the Advantages of OCR in B2B?

Here are the major ways implementing OCR can benefit your business processes:

  • Enhanced Customer Experiences: By automatically extracting order data, OCR enables rapid order processing and fulfillment. This allows you to deliver products or services to customers sooner, improving satisfaction and retention.
  • Increased Productivity: OCR eliminates slow and tedious manual data entry and frees up employee time for more value-added tasks, boosting productivity.
  • Improved Accuracy: Even trained data entry staff make mistakes occasionally. However, compared to manual entry, OCR software can achieve extraction accuracy of over 90%.
  • Lower Costs: While requiring some upfront software and hardware costs, OCR pays for itself by reducing labor expenses and document storage overhead. The hours saved in transcription labor can add up to major cost savings.
  • Enhanced Data Security: Digitized data can be backed up securely and controlled through permissions/encryption, which reduces security vulnerabilities compared to paper.
  • Better Analytics: Digitizing data allows better reporting, segmentation, search, and analysis capabilities via business intelligence tools.
  • Environmental Benefits: Transitioning from printing and posting documents to digital storage saves on expenses like paper, ink, mail costs, and physical storage space.

What Are the Challenges and Limitations of OCR?

While delivering value, traditional OCR solutions have notable drawbacks that prevent them from being a cure-all for business document processing needs. Here are the major pitfalls businesses should understand when evaluating OCR tools:

  • Limited to Structured Data: OCR struggles with unstructured data like images, charts, and tables without additional software. This requires manual data prep or custom automation to get around OCR's limitations.
  • Font and Format Challenges: Substantial variations in document fonts, languages, layouts, and formats can flummox OCR. Complex documents cause exceptions that require manual verification/fixes.
  • Lookalike Character Errors: OCR solutions have difficulty distinguishing lookalike characters like "l" and "1" or "0" and "O," leading to recognition errors that impact data integrity.
  • Image Quality Constraints: Factors like document orientation, folds, creases, background colors/images, bleed-through ink, and the quality of scans all undermine accuracy.
  • Less Than 100% Accuracy: The best OCR solutions can only achieve

    98-99% accuracy

    , which equals 200 errors for every 10,000 characters. In practice, this can mean dozens to hundreds of errors per document, requiring manual verification and correction.

When emailed purchase orders overwhelmed staff, Häfele America turned to Conexiom's AI-powered document processing solution instead of OCR. Conexiom’s automated order handling boosted efficiency, allowing employees to shift focus to improving the customer experience.

Overcome the Challenges of OCR with Automated Order Processing

AI-powered solutions, such as Conexiom, overcome the accuracy and flexibility limitations of OCR solutions thanks to intelligent document processing capabilities. Let’s look at the advantages offered by automated order processing:

1. Enhances Accuracy

Leveraging machine learning and natural language processing, AI solutions can extract data with 100% accuracy, even on complex documents. Advanced algorithms validate data in context to resolve uncertainties that might trip up traditional OCR, reducing the need for manual verification.

2. Adapts to Diverse Documents

With training on vast amounts of data, AI document processing readily handles diverse formats, fonts, languages, and more. It can extract information from documents with multiple languages, stylized fonts, multi-column reports, tables, and other elements that undermine legacy OCR accuracy.

3. Handles Lookalike Characters

AI models develop a deeper understanding of character shapes and meanings in context, easily distinguishing between 1's, l's, and I's. This prevents errors from creeping into text extraction.

4. Overcomes Image Quality Issues

Unlike template-matching OCR, AI document processing handles variations in image quality, orientation, background, and artifacts. It smoothly deals with folded, creased, or poorly scanned documents that traditional OCR would struggle to interpret.

5. Provides Easy Integration

Modern AI extraction solutions provide user-friendly APIs to integrate with existing business systems, ERP platforms, etc. This enables seamless end-to-end intelligent document processing at enterprise scale.

Genpak eliminated inefficient manual order processing with Conexiom's AI-powered data extraction. Processing orders accurately in minutes, Conexiom freed up 75 hours a week for Genpak's CSRs to devote to meeting customer needs instead of data entry.

How Conexiom Outperforms Traditional OCR

When assessing document digitization strategies, it's clear that AI-powered solutions like Conexiom go far beyond the capabilities of traditional OCR solutions. The Conexiom platform was purpose-built to replace manual sales order and invoice handling with intelligent automation and allow you to unlock the value of your documents at an industrial scale.

By leveraging machine learning rather than rigid rules-based scripts, Conexiom provides:

  • 100% accurate data extraction
  • Smooth handling of diverse formats, fonts, languages
  • Automated validation routines to ensure error-free extraction
  • Seamless integration with existing systems
  • Learning capabilities that improve continuously over time

Book a demo to see firsthand how Conexiom can help your business eliminate paper-based bottlenecks, enabling growth and efficiency gains.

Looking for more content?

Looking for more content? Get updates sent to your inbox