How To Improve The Accuracy of Intelligent Character Recognition (ICR)

How To Improve The Accuracy of Intelligent Character Recognition (ICR)
5 min read
11 February 2021

From start to finish the data management history businesses are discovering more efficient ways of data management and data processing. The invention of the printing press was a huge advancement in data storage. It helps in the storage of large scale data at the commercial level. Because of the printing press, manual data writing was reduced heavily. The newspaper industry used that service for printing newspapers. The process was fast and robust so was well appreciated by all businesses.

Then the typewriter was introduced in 1874, which gave a boom to writing personal notes. Now common people can also convert their handwritten documents into typewritten. Writers and researchers used a typewriter for writing books and research purposes respectively.

Intelligent Character Recognition (ICR)

Initially, OCR can convert grey-scale documents into digital form. But it updates as time passes OCR becomes more mature. At present time it can extract data from colored documents also. OCR can convert typewritten, handwritten, and printed documents into computerized form. It works perfectly on unstructured and semi-structured documents. But its ability to understand unstructured data was limited.

To give accurate results on unstructured data, OCR was empowered with Artificial Intelligence (AI) and named Intelligent Character Recognition ICR. ICR has the ability to extract data from every document and in every language. It can understand all the scripts and fonts of writing styles. The thing that makes ICR more good as compared to OCR is self-learning of new writing patterns. It can test and train on new data by itself with the help of AI and Machine Learning (ML) algorithms.

The range of intelligent character recognition software is limited because of less data for its training. As time passes it will become mature. As reported by Accusoft, the accuracy rate of ICR is 70%. This means that three out of ten words are recorded incorrectly. ICR needs to be trained and tested on more data sets to give more accurate and reliable results. The accuracy rate of ICR technology can be improved by the below steps:

Improving Field Designs or Smoothening the Comb Lines

The fields in which the data is to be inserted should be improved. Some websites exactly give freehand on fields so that the user can enter data according to his desire. This may look satisfied but extracting data through those fields is difficult because of poor design structure. Like address fields should be left blank, proper fields should be designed like country, state, city, and street. Combine Lines are the horizontal lines with vertical cuts that separate one data field from another should be smoothened or their size should be large and clear. Unclear comb Lines create difficulty for online intelligent character recognition in data extraction.

Data Constraints

Proper constraints should be added like special characters or numbers should be allied in the name section and alphabets should be allowed on the mobile number field. The constraints can be easily added while designing the database of the website.

Proper Font Thickness

Fonts should have the proper thickness and bold fonts should be used in case of critical information. The design of fonts directly affects the data extraction process by causing troubles for ICR. if the fonts are not clear or have low megapixels, it can cause errors in data extraction.

Proper Margins and Alignment

The regular margins used in web page designing, with well-defined data fields. Because of margins, users are restricted to enter data in a specific region of the page. It creates ease for ICR because now it does not have to align the page. Plus the data is in regular form or structured form that produces more efficient results using ICR.

More Testing and Training

The best way to improve ICR efficiency is by training it on more data sets. As ML algorithms become more mature if trained on large data sets. The previously present hard data and its digital form should be given to the ML model. As ICR has AI and ML, it can train itself on these data sets. The more training the more accurate results will be produced.

Summing It Up

ICR will take time for producing efficient results. Large organizations are working on it. Till that time businesses should use OCR Technology for data extraction and data processing. The identity verification industry, Fintech, and health industry is using OCR. it is giving nest results to those sectors.

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Ryan Jason 7
Technical Content Writer
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