SharePoint Syntex is an M365 Service that uses AI and Machine Learning Models to augment human experiences, content processing, and knowledge transformation. It does not necessitate the creation of any code, unlike other machine learning algorithms. It automates the organization, classification, and tagging of structured, unstructured, or semi-structured documents in document libraries.

SharePoint libraries have long been used as a file repository, but there was no way to organize, classify, sort, and group documents based on their content.

SharePoint Syntex solves the content extraction problem and implements classifications It not only aids in content extraction, but it can also generate the document based on the contents stored in the database.

For classification and extraction, SharePoint Syntex already includes two prebuilt models.

Document understanding

The document comprehension model is used to extract information from unstructured documents such as contracts or letters. These documents must contain text that can be recognized using a pattern or phrase. When this model is applied to a document library, the extracted information is stored in the columns that correspond to a content type. It can also create a new content type or use an existing one.

Form processing

SharePoint Syntex creates a model in SharePoint document libraries using MS Power Platform AI builder form processing. Users can use the AI builder to create a model that extracts key-value pair or table data from unstructured documents such as forms, purchase orders, or invoices. The same information can be saved in the SharePoint list.

“import easyocr
reader = easyocr.Reader([‘en’])
Result = reader.readtext(‘abc.png’)
Print(result)”

OUTPUT: [([[272, 304], [446, 304], [446, 336], [272, 336]], ‘Hello World!!’, 0.737285318007567)]

This kind of output could be difficult for the non-developers to understand hence we can make it simple by passing the detail parameter as 0.

“import easyocr
reader = easyocr.Reader([‘en’])
Result = reader.readtext(‘abc.png’, detail=0)
Print(result)”

OUTPUT: [‘Hello World!!’]


The output is not showing the coordinates of the text as we pass the detail parameter as 0. It is only showing the required text. In this code ‘en’ is english language we can write any language in which we want to extract text.

Use Cases of OCR

  • Converts hand-written text to machine-readable text.
  • Invoice processing.
  • Traffic sign Recognition.
  • PDF extraction.
  • Number Plate Recognition.
  • Digital Document Scanning.

Disadvantages

  • OCR cannot read hand-written text.
  • OCR images needs lot of memory space.
  • OCR systems are quite expensive
  • OCR systems are not 100% accurate.
  • OCR needs a good quality image to work well.

How OCR helps in solving Business Problems?

OCR helps in Business to become more productive and cost-efficient by making easy to process documents and digitize them.

  • Converts Document to editable and searchable data - We can get any type of document it can be paper document, image, by email or hand-written data. OCR digitize all your data and make it easy to edit or search for the information we are looking without consuming lot of time and efforts.
  • Accelerate speed of Document processing
  • Improve data Security- OCR uses AI and Machine Learning to recognize sensitive information and protects from unauthorized employee to access the information.
  • Reduce Cost- OCR assist Business to reduce cost by improves accuracy of Data entry and save money spent on manual entry also reduce errors
  • Retrieve information faster- Employees usually spend a lot of time in retrieving information from documents. When the document is very large it can be very useful in making information more accessible for employees and making decision faster so that they can focus on more important tasks.

Conclusion

EasyOCR is the simplest and easiest way to implement Optical Character Recognition (OCR) with very few lines of code. Dealing with images becomes simple and quick. A large amount of text can be processed quickly. The information obtained through OCR is then more understandable and accurate. OCR is more accurate and takes less time than manual typing. It contributes to improved performance accuracy.

If you want to learn more about such topics or if you need software consulting, please contact us at [email protected].