Regex, also known as “regular expression” is a pattern-matching technology used to find and extract text within a document. It is a sequence of characters that specifies a search pattern. Regular expressions are widely used in document processing to find and extract texts that follow a pattern.
Regex and OCR for processing documents
regex is widely used in processing documents in conjunction with the OCR. OCR returns texts from PDFs or scanned documents in a linear form and we can apply regex to extract data that follows a pattern. For example, email addresses follow a pattern that includes characters before the “@” sign, followed by a domain name and domain extension has a pattern such as “.com” or “.org” etc.
Regex in document processing
Regex is a quick and easy solution for document processing for simple and straightforward data extraction. We should consider a few factors while using regex in document processing.
Where to use regex in document processing?
Firstly, Regex works best when the data can be identified by clear patterns rather than by context. If the data you’re trying to extract relies heavily on context, such as natural language processing (NLP) tasks, Regex may not be the best approach.
Secondly, the effectiveness of Regex also depends on the type and variety of documents you’re working with. If you’re dealing with a large number of different document types with varying layouts and structures, Regex may not be able to handle the complexity. Similarly, if there are multiple variations of the pattern you’re trying to extract, Regex may require a lot of customization and fine-tuning.
Lastly, while OCR (Optical Character Recognition) accuracy is important for extracting data from scanned PDFs or images, it’s not the only factor to consider. If the OCR accuracy is low or if there are other types of noise or errors in the scanned document, Regex may not be able to accurately extract the desired data.
How Regex can classify documents?
Regex can be useful in classifying or categorizing documents based on predefined patterns. When the number of document types is known, static, and less, Regex can be a quick and effective solution for identifying and categorizing documents based on specific identifiers. However, if the document types are unknown, numerous, or complex, Regex may not be the best approach.
We can pick up one or multiple identifiers from documents and then using Regex pattern matching, easily identify the document type. But if the document types are unknown and data extraction needs to be on a contextual basis, Intelligent Document Processing (IDP) may be a more appropriate solution. IDP utilizes advanced machine learning algorithms to extract, classify, and categorize data from complex documents, including unstructured data, handwriting, and more.
Regex and IDP for document processing
IDP and Regex are not mutually exclusive solutions. In fact, IDP may utilize Regex as part of its data extraction and classification processes. Therefore, it’s essential to evaluate the document types, data sources, and specific requirements before determining the most appropriate solution for a given task.
Regex can be a useful tool for simple and straightforward data extraction tasks, it’s important to consider the complexity of the documents you’re working with, as well as the accuracy and quality of the data sources.