Why develop custom OCR solution using machine learning?

OCR has been the “talk of technology” in today’s ERP systems. OCR stands for Optical Character Recognition. To tell you what it does, it leads a business to process incoming documents like customer orders, receipts, invoices, scanned reports etc. into digital data. This data can be further accessed and edited automatically in ERP systems. OCR has enhanced the way digital scanning works unimaginably!

The Evolution

It’s the ‘Information Age’. Everything we know today runs on intelligence. So, why shouldn’t OCR have one?

A normal OCR remains good to the value to an extent. After a point, it reaches its limits. A normal OCR fails to process complex and unstructured documents. What if somehow the OCR knew what to grasp from the document and what to process on its own? Wouldn’t that be groundbreaking? By giving an OCR ‘a brain’ of itself, it can read unstructured, complex and noisy data with ease.

An ML-driven OCR provides:

• high-accuracy extraction
• document classification to any level
• data cleaning
• data validation
• context preservation
• predictive insight
• anomaly detection
• insight generation

The Future Market

It is speculated by Transparency Market Research that by 2025-end, the value of the OCR industry will peg at $25.180 billion, and will grow annually at 14.8% from 2017 to 2025.

Also, the Asia-Pacific OCR market is expected to outpace the market by 15.6% from 2017 to 2025. The spread of digitisation and the increased availability of optical character recognition software is catalysing for the Asia Pacific market. The fast-emerging markets of China and India are at the forefront, given their diversity of languages, as well as investment in technology.

nCircle’s ML-powered OCR

nCircle — a world-class 3D engineering software company has developed an OCR with the help of artificial intelligence. Our ML-powered OCR solution looks at an object as “a whole”, and scans all the available data on the document and considers every word as interrelated to each other. Our software is designed in such a way that it fills in the void of understanding and simplification. Using available data and learning our ML-based OCR is on the level of ‘Human-Like’.

With our advance ML-based OCR, you get the benefits of:

• Faster Search – Once the information is stored, the employee can access it anytime just by searching the same.
• Reduced Errors – All the data are scanned and validated, thus reducing errors which could have been caused by employees during the correction.
• Elimination of Manual Entries – Eliminates manual entry by capturing data automatically through scanning. Thus, diverting efforts and time of employees on core competencies.
• Efficiency and Economical – OCR helps in reducing several expenses on printing, copying, and file cabinets. Thus, allowing expenses on needful accessories.
• Ready Availability – All the scheduled tasks are executed on time so that business stays on plans & strategies to achieve the desired goal within a specified period.

To get a more detailed insight on our ML-powered OCR, head to our recent blog post on its functionalities.

User Case Scenarios

Large construction projects generate thousands of documents that require careful management. The classification of documents is an important step in document management and control. Construction documents are generated in different formats, many of which are unstructured and contain drawings and images, which makes the task of document classification and control even more challenging.

Let’s see how our ML-based OCR fared in this scenario.

Overview:

Field engineers and contractors have to manually verify and copy the text data available from the asset plate and labels. This activity, when done manually, is less optimal, leading to human errors and time wastage. 

So, this particular organization’s main workflow includes populating asset data from information taken from approved submittals. Then by utilizing the mobile app to verify the data against the asset nameplate, confirmation is taken if the correct asset is installed along with it. Further, additional data such as the serial number and manufacturer data from the nameplate are also captured and recorded. 

 So, where did technology fail short to achieve the organization’s streamlined workflow? As the employees were handling the data capturing manually, there were many errors, which led to less productivity, waste of resources, and labour.

Requirement:

They needed a solution that:

• Complement their workflow and make the process of data verification and collection faster, easier, and considerably more accurate.
• Identify and extract the data across infinite variations in nameplate styles and layouts.
• Process standard black and white images with clear labels as well as embossed paper and metal plates without clearly labelled identifiers.
• Account for poor lighting, focus, image quality, and camera angle perspectives.

Solution:

With the help of our OCR solution, the organisation was able to achieve their requirement. Our solution captured and converted tons of construction images into a well-categorized text format. Thus, making them useful to verify information captured from the submittals as well as populate the additional fields including serial number and manufacture date. This format became easy to edit, expand and share. It further boosted productivity and improved the documentation, thus, enhancing efficiency through:

• Accuracy in the interpretation of data
• Ability to read text despite multiple alignment or orientations in one document

Check out the video to see how our ML power OCR simplified the shortcomings.

VIDEO –

Outcome:

It is speculated that by 2020 the organisation will participate in over $5 billion worth of construction projects. From those projects, approximately 50,000 assets delivery that requires nameplate processing will take place.

Time savings and Accuracy:

With their manual verification and data capture, it used to take on average 2 minutes to process the asset nameplate data including verification of the existing information, new information and a post-processing verification in the office. Now, with the new ML solution, it takes on average of 4 seconds. Thus saving approximately 1600 man-hours. With ML-based OCR, the organisation saw more than a 70% reduction in data errors.

Why was nCircle’s OCR chosen over Google & Azure-powered OCR?

Google and Azure OCR, gives you text as they interpret it. When a traditional OCR solution gives you a bunch of characters, nCircle’s OCR tries to correlate those and map characters that should correspond to the model number. This also means that there’s no human intervention in between of this, thus making the process a whole lot easier.

Also, nCircle’s OCR engine is specialized for construction data. It has higher accuracy as compared to generic OCR engines. Thousands of nameplates are used to train the OCR Engine to logically identify the needful data.

Closing Thoughts

Now you have read about how our ML-powered OCR transcends the limitations and provides a precise, accurate and quick response to scanning documents, why not join us at the Autodesk University 2020 to know more about it. You can also head to our website and see what more is there for your business in our basket? Check out the benefits, scope and connect with us so that we keep leading by creating impactful 3D engineering & construction solutions.