{"id":550,"date":"2021-08-18T15:09:15","date_gmt":"2021-08-18T15:09:15","guid":{"rendered":"https:\/\/www.softage.net\/blog\/?p=550"},"modified":"2021-08-18T15:09:20","modified_gmt":"2021-08-18T15:09:20","slug":"a-comprehensive-guide-to-ocr","status":"publish","type":"post","link":"https:\/\/www.softage.net\/blog\/a-comprehensive-guide-to-ocr\/","title":{"rendered":"A Comprehensive Guide to OCR"},"content":{"rendered":"\n

Technologies are changing lives everyday. We can see great\nto greater achievements in technology with every coming single day. With the\nrole of internet, every platform is moving from offline medium to online medium.\nIt is observed that customers are quite satisfied online as it fits their busy\nand scheduled lifestyle. The usage of a technology named OCR is increasing day\nby day. In this blog we will discuss about the technology known as OCR.<\/p>\n\n\n\n

What is OCR?<\/h2>\n\n\n\n

OCR stands for Optical Character Recognition. OCR is basically process that creates an electronic version of a self written, hand written document. In simple words, it can also be seen as a scanning software that converts the offline version of document into electronic media. OCR is backbone of document digitization<\/em><\/strong><\/a>. \u00a0<\/p>\n\n\n\n

As the world is moving towards an Internet era, offline\ndocuments are being inaccessible. To make use of essential offline documents conversion\ninto machine readable version is very important. OCR scans a document and\nconverts hand written or typed offline document into machine-readable text. This\nmachine readable text then is converted into desired format like pdf, jpeg. So,\nin simple words we can say that OCR systems convert a two-dimensional image of\ntext into machine-readable text, which could include machine-printed or\nhandwritten text.<\/p>\n\n\n\n

Process of OCR<\/h2>\n\n\n\n

We understand the fact that OCR scans and digitizes the\noffline material to online, but what about its process. Here is a list of sub\nprocesses of optical character recognition that compiles to get the best\ndesired output- <\/p>\n\n\n\n

Image Scanning<\/h3>\n\n\n\n

First and the foremost step is to scan the image properly.\nIf the document is not scanned properly, or we say we have unclear scanning will\nlead us nowhere. Clear and proper scanning is very essential for our process to\ngo spontaneous. <\/p>\n\n\n\n

Image Processing<\/h3>\n\n\n\n

Then the further processing of image takes place. An image\nis created as normal scanners create a virtual image of the scanned offline\nrecord.<\/p>\n\n\n\n

Text Localization<\/h3>\n\n\n\n

Text localization is considered to be the one among first step where machine learning and artificial intelligence<\/a> comes into play. Localization is clustering the text of the documents. <\/p>\n\n\n\n

Text Recognition<\/h3>\n\n\n\n

Text recognition is the essential sub step enabled with\nmachine learning. In this step, the virtual image texts are recognized on basis\nof AI and machine learning. <\/p>\n\n\n\n

Character Segmentation<\/h3>\n\n\n\n

Character segmentation is the process in which recognized texts are set and indexed as per the word format. This step is initiated to make sure that meaningful words are set as it is they were present in the offline version.<\/p>\n\n\n\n

Post Image Processing<\/h3>\n\n\n\n

In this sub set image is processed after completion of every other sub set. Image is processed enabling machine learning and AI. Clustered and recognized text is then segmented to form the best possible output of an electronic document. <\/p>\n\n\n\n

Storage<\/h3>\n\n\n\n

The last step of the complete process is to store the converted document into database. Different types of data bases are used to store the document. Cloud storage is a trending technology to store documents online for any time use. <\/p>\n\n\n\n

Benefits of Integrating AI with OCR<\/h2>\n\n\n\n

Process Automation<\/h3>\n\n\n\n

Integrating bots for document interpretation is important\nsince it automates the entire process from beginning to end. All we have to do\nnow is set up a learning workflow for the bots and sit back and relax. During\nthe validation process, we may need to rectify any issues that the bots have\nfound, such as errors or frauds.<\/p>\n\n\n\n

Deployment<\/h3>\n\n\n\n

After the pipelines have been constructed, the deployment\nprocedure takes less than a minute. We can have bots export APIs after they’ve\nbeen trained, or we can design a custom RPA solution that can be used in our\nown systems. This form of deployment can also help businesses streamline their\noperations and cut costs while posing relatively few risks.<\/p>\n\n\n\n

Enhanced Processing<\/h3>\n\n\n\n

We’ll need to construct separate deep learning pipelines for\ndifferent types of documents for general tasks like table and information\nextraction. This necessitates the development of many apps and the deployment\nof various models on various servers, which takes a significant amount of time\nand effort. We can also use APIs to combine various services and communicate\nwith other businesses in terms of data retrieval.<\/p>\n","protected":false},"excerpt":{"rendered":"

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