OCR

By Aaron Bianchi
May 5, 2023

In today’s fast-paced digital world, document processing is a must-have for organizations to remain cost-effective and efficient in their operations as possible. Optical Character Recognition (OCR) and Machine Learning (ML) are two technologies that have significantly improved the speed, accuracy, and overall efficiency of document processing.

OCR and ML technologies have become increasingly popular in the last few years, enabling organizations to automate repetitive and time-consuming manual tasks. They allow organizations to convert paper-based documents into digital format, recognize and extract text and data, and automatically classify and organize them.

In this article, we will explore the benefits of OCR and ML in document processing and how they can help organizations to improve their workflow and productivity.

1. Faster Processing Time

   OCR and ML technologies automate the conversion of paper-based documents into digital format, which significantly reduces the time required for manual data entry. With OCR, documents can be scanned and converted into editable digital files within seconds, making it faster and more efficient than manual data entry.

   ML, on the other hand, can help to automate complex tasks such as document classification and data extraction. By training ML algorithms on a large dataset of documents, organizations can teach machines to recognize patterns and make predictions about new documents, reducing the time required for manual document processing.

2. Improved Accuracy

   Manual data entry is prone to errors and can be a time-consuming task. OCR and ML technologies have significantly improved the accuracy of document processing by reducing the risk of errors and inconsistencies.

   OCR technology recognizes and extracts text and data from documents with high accuracy, reducing the need for manual data entry. ML algorithms can be trained to recognize specific patterns and keywords in documents, making it easier to extract and classify data accurately.

3. Enhanced Document Security

   OCR and ML technologies can improve document security by enabling organizations to store and manage documents securely. With OCR, documents can be converted into digital format and stored securely in the cloud or on-premise servers.

   ML algorithms can also be used to detect anomalies in documents, such as unusual patterns or changes in text, making it easier to identify potential security threats. By implementing OCR and ML technologies, organizations can improve the security and privacy of their documents.

4. Cost-Effective Solution

   OCR and ML technologies offer a cost-effective solution for organizations that need to process a large volume of documents regularly. By automating document processing, organizations can reduce the need for manual labor and minimize the risk of errors and inconsistencies.

   OCR and ML technologies are also scalable, making it easier for organizations to handle document processing at any scale. By implementing OCR and ML technologies, organizations can achieve significant cost savings and improve their bottom line.

Conclusion

OCR and ML technologies have revolutionized document processing, making it faster, more accurate, and cost-effective. By implementing these technologies, organizations can improve their workflow, productivity, and bottom line.

In summary, OCR and ML technologies offer the following benefits:

• Faster processing time

• Improved accuracy

• Enhanced document security

• Cost-effective solution

By embracing these technologies, organizations can stay ahead of their competitors and achieve success in today’s digital world.

By Aaron Bianchi
Apr 29, 2021

As a teenager in the 1970s I worked for an early Optical Character Recognition (OCR) company. They had an SUV-sized scanner in their computer room that digitized IBM Selectric double-spaced Pica text with about 80% accuracy and printed it to microfiche. I learned to program the DEC VAX that drove the scanner by typing octal instructions onto paper tape and then bootstrapping the tape reader. I also spent many hours in the proofreading pool comparing the microfiche output to the source data, the Manhattan White Pages, and logging corrections.

OCR has come a long way since then.

Today’s OCR is an application of computer vision that enables machines to find and extract text embedded in images. OCR projects are seeing explosive growth because of their potential for reductions in the cost of human labor and human mistakes and increases in productivity and security.

Real-world examples of OCR are legion:

• Many autonomous device use cases demand an ability to read text in the form of signage, warnings, and surface-embedded instructions

• Industries like real estate and financial services want to reduce or eliminate human involvement in digitizing business documents and other artifacts and electronically capturing the business-critical content therein

• Likewise, many industries are seeking to eliminate the need for humans to interpret and process handwritten content like patient charts, whiteboard sessions and annotated text documents

• Other examples include license plate recognition, menu digitization, language translation, and many more

OCR models are a subset of machine learning models, and more and more, deep learning OCR is data scientists’ preferred approach. The complexity and nuance of real-world OCR tasks gives deep learning models an appreciable performance edge.

Deep learning models don’t train themselves. They, too, require training data, and feedback and refactoring, to achieve optimal outcomes. And in fact, their performance edge comes at a cost: deep learning OCR requires significantly more, often orders of magnitude more, training data than many other ML approaches.

OCR involves two steps, and OCR models must be trained in both. A trained model has to identify the location of salient text in an image, referred to as text detection, and it must perform text recognition, the extraction of text content.

The very large quantities required aside, OCR training data is produced in standard fashion. Human data labelers annotate input images, typically with bounding boxes or polygons, to localize text areas. The particular application may require that they separately label different text areas or indicate how text blocks are related.

Importantly, labeling and annotation is just the final step in training data preparation. Many data science teams work with data collections that include input images that are distorted, skewed, or inconsistently lit or sized. Yet other teams are confronted with very large quantities of paper that have not been digitized.

Training data partners that can supplement OCR training data labeling with a full complement of data curation and data creation services offer data science teams a significant leg up with regard to their OCR projects.