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How to Prepare Data For OCR Learning

DZone's Guide to

How to Prepare Data For OCR Learning

Let's take a look at how to prepare data by first selecting it, then preprocessing it, and lastly, transforming it.

· AI Zone ·
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Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Data analysis without data preparation is a myth. Unless we feed the right data in a proper format, Machine Learning algorithms won’t be able to solve our problem. If we give one wrong input then we end up where we started. So it’s very important to understand what data preparation is and how one can do it.

Data in its original form may have a lot of missing pieces or disarrangement. Through data processing, one can modify this raw information from a specific database to a format which is understandable and learnable by the machine. Mentioned below are the ways that one should prepare the data.

Sample data for OCR learning


1. Data Selection:

It is necessary to first identify the type of data we are going to be working with. One has to keep in mind whether the available data will be able to address an existing problem or not. We keep certain factors in consideration before selecting the data:

  1. Data should not be of low quality: Low-quality input = low-quality output.
  2. Dataset is not error-ridden: The more the errors, the more time it takes to preprocess it.
  3. Dataset is unbiased: Having an unbiased dataset opens new doors in terms of discoveries in predictive modeling.

2. Data Preprocess:


Infrrd’s Data Preprocessing Model


Once we have selected the data, we determine how we will be using it. In this step, we transform the data into a format that would be compatible for our future use. There are 3 ways to preprocess data:

  1. Format: Since the raw input is not in a usable format for OCR learning, formatting it ensures that machine learning algorithms can comprehend it to solve the issue. For example, the formats of date and time etc. needs to be consistent throughout the dataset.
  2. Cleanse: Here we remove the missing data or the irrelevant ones. It also involves fixing structural errors like typos and inconsistent capitalization, mislabelled classes etc. Here data wrangling tools, or batch processing through scripting becomes essential.
  3. Sampling: Often there is more information available to us than we actually require. Via sampling, we obtain a smaller portion of the data which gives us prompt prototype results from the algorithms and speeds up the entire data mining process for OCR learning.

3. Data Transformation:

This is the final step wherein we receive the modified data for machine learning. Sometimes we may need to go back to preprocessing information just to make sure that we have the right kind of information for the specific algorithm or problem domain we are working on. There are 3 data transformation procedures that we use:

  1. Center and Scale: Preprocessed data will more likely contain a mix of scales such as currencies, weight, height etc. By centering and scaling the data using mean and standard deviation respectively, these variables could be standardizable.
  2. Decompose: Through this procedure, complex data concepts are fragmented and segregated into more specific segments to achieve a more useful machine learning format. It is also called data bucketing.
  3. Aggregate: This step allows information to be gathered and expressed in a summarized pattern. The bulk data can be grouped by segmenting it into broader aggregates with similar attributes reducing data size and computing time.

In general, data preparation is a big, non-fancy task in the OCR machine learning, involving some repetition, exploration, and inspection. Using machine learning and NLP, we have built context around the prepared data for easy inference, to accurately extract and predict data simultaneously while learning from scores of data sets. Thus, the data we extract is 50 times more accurate than any other OCR solution in the market.

We have learned from scores of enterprise data, thereby, making its results more than 98% accurate for most samples. This can apply to many enterprise processes like:

  • Banking — Processing handwritten checks and documents
  • Finance — Invoice, receipts and mortgage documents processing
  • Manufacturing — RFP processing
  • Healthcare — Insurance forms and general health forms processing

Click here for a free OCR demo

Start coding something amazing with the IBM library of open source AI code patterns.  Content provided by IBM.

Topics:
artificial intelligence ,ocr ,machine learning ,enterprise ai ,text analytics ,deep learning ,data processing

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