Checkboxes are relevant to the documentation process. While regular data extraction methods work well with text or any textual data, checkbox detection often seems to come up as a hurdle. In this blog, we discuss checkbox detection methods that are powered with or without deep learning.
Document processing for any organization is a collateral. Over the years, tasks related to document processing have been completed using traditional methods i.e. manual processing, OCR, or both.
Yet with time, the requirements today demand a much more solid solution. While multiple document processing solutions provide data extraction for texts, they still struggle with use cases such as checkbox detection.
So to further expand on the topic and explain checkbox detection with deep learning, we have written this article. Read ahead…
Checkbox detection is the process of detecting checkboxes and the data they represent. In multiple industries and organizations checkboxes are used with forms and documents to fasten the process of selecting services, terms & conditions agreements, picking out existing ailments (in healthcare) etc. These are easy to understand by the customer and makes the process much more cleaner & efficient for the organization.
When it comes to data extraction, there is a variety of data that needs to be extracted from a document such as text, tables, forms, key value pairs, etc. However, the situation becomes tricky when extracting non-textual information from a document. An OCR isn’t simply capable of doing it since it primarily works for text and has the tendency to show skewed results with varied document types.
Aside to this, from an SME to a large organization, the variety of documents that are processed is very huge in number. Also, the data within them including the key values of the checkboxes changes with different vendors, suppliers, and organizations. To further consolidate the issues, here is a list mentioned below:
Checkbox detection poses as an anomaly behind multiple textual data. Also, it won’t be ideal for a company to establish a solution for textual data extraction but check the documents for checkbox detection manually.
To curb these problems and provide end-to-end automation for faster realization of the process, checkbox detection automation was introduced. For checkbox detection, document processing automation platforms such as VisionERA uses Computer Vision with deep learning. However, there are other ways to do that also.
With computer vision combined with deep learning, it is possible to extract relevant data from the checkboxes and store it to the central database. The experience is flawless providing scalability, flexibility, along with minimal intervention.
Computer vision is a branch of artificial intelligence. It gives artificial machines aka softwares/platform/applications the capability to derive meaning from images, videos, and other visual inputs. The great thing about computer vision is that it can work in unison with technologies such as deep learning, natural language processing, OCR, etc.
For platforms such as VisionERA Intelligent Document Processing, computer vision provides the capability to detect information for use cases such as checkbox detection. It is because the data that needs to be extracted may or may not be textual.
As mentioned before, the biggest problems that today’s enterprise has to face is the capability to process unstructured documents with least TAT. With the use of technologies such as deep learning or machine learning, document processing automation platforms are able to bridge the gap and are capable of providing a cognitive & intuitive solution.
Note: Deep learning is an extension of machine learning, therefore, it can be used interchangeably in many places. The basic difference is deep learning forms a much larger neural network for processing or conditions to reach the most optimized route.
With deep learning, there can be a vast majority of methods to deal with a single problem. The thing to note here is that checkbox detection is a binary use case in a particular sense. It means that the conditional statements either need to be true or false. Based on that models and methods can be derived to solve the problem.
An important thing to note here is that the method utilized for checkbox detection will directly affect the input and the accuracy of the model. Also different companies and organizations may have different requirements, therefore, the model developed needs to be well-versed, customizable, and should provide the necessary accuracy numbers.
The two approaches or methods that can be used are:
The technique utilized for extracting relevant checkboxes automatically, requires primarily two factors:
Levenshtein Ratio: In simple terms, it is the ratio of the distance between two words. Here, it is will utilized for determining the distance between the extracted textual data and the checkboxes.
Data Labeling and Offsets: As mentioned earlier, the results in this form can be treated as binary. A filled checkbox can be labeled as “X” and the unfilled offset can be labelled as “Y”.
By using the OCR and the levenshtein ratio, the empty checkboxes can be easily determined from the filled ones. Also premeditated entries in the database can be filled with the marked checked boxes data using an API to the central databases.
However, there are certain limitations with this method:
There are definitely few ways to improve the accuracy of this model, yet it won't be worth the time. It is because the primary reason why organizations are moving towards automation is huge volumes of unstructured data. Also for different templates, the OCR will require specific training which will add both time and cost to the process. Even if deep learning is applied, the process will be automated but the accuracy with checkboxes can still take a toll depending on the quality of the input.
With computer vision developers have the capability to use deep learning to develop a much more scalable product. For checkbox detection approaches such as determining horizontal lines, vertical lines, edges, contours, etc. will be utilized.
Below are the steps to identify a checkbox using computer vision:
In this step, the developer will import the necessary libraries for the computer vision algorithm to work. There are multiple technologies that are out there that can work in combination to provide the necessary output. The most common combination is Python and OpenCV.
Transformation of the image and feeding it to the image array for further processing.
In this step, the task is to separate the foreground of the image from its background for maximum clarity. It can be done by converting the RGB (red, green, blue) values of the image to grayscale as they are less skewed and less noisy. The process is known as image binarization.
This step will utilize special filters and extraction of the horizontal & vertical components of the image. These morphological operations will aid in forming a square or rectangular box around the checkboxes.
With this step, the deep learning model will figure out the contours from the process. These contours will be marked on the image as checkboxes helping the model to identify where the checkboxes reside in the document.
In this final step, the OCR module will figure out all the marked and unmarked checkboxes. Using deep learning each of these boxes can be corresponded to their labeled entry.
VisionERA comes with the feature of custom DIY workflow. It means the user can create its own workflow and establish its own global values for each checkbox label. It is a minute's worth of manual work for any template.
Another technology that VisionERA is backed with is Natural language processing. This technology helps VisionERA to determine context from the document’s checkboxes, thereby, helping them associate with the labeled entries in the document to the predetermined global entries. We’ve already explained how the computer vision algorithm will work on a template. Combining the features of VisionERA, embedded computer vision algorithm, and deep learning, the platform will be able to extract all the marked checkboxes. From here, the data can be stored directly to the central database or downstream application using APIs.
There are several benefits of using VisionERA for checkbox detection such as:
With deep learning, organizations will be able to stay ahead of the collateral hiccup i.e. checkbox detection. It will allow organizations to utilize their full workforce for intellectual tasks and will save money in manual operation. With IDP platforms such as VisionERA, checkbox detection can be simplified and organizations can fulfill their goals with much faster processing time and ease.
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