In the age of digital transformation, where every piece of information is becoming rapidly accessible and organized, business cards remain one of the few tangible pieces of professional information exchange. While their physical form offers a personal touch, extracting information from them in a quick and efficient manner poses a unique challenge. To address this I have thought to write my approach for business card text extraction in the best possible manner.
In the powerful combination of Natural Language Processing (NLP) and Optical Character Recognition (OCR), NLP enables machines to understand and respond to human language. On the other side, OCR technology converts different types of documents, including scanned paper documents, PDF files, or images taken by a digital camera, into editable and searchable data.
In this blog, we will delve into an innovative method that combines the strengths of both NLP and OCR, specifically the renowned Tesseract-OCR tool, to extract and categorize information from business cards. From identifying specific phone numbers such as office, fax, or mobile numbers to precisely extracting detailed address components like city, state, and country, this technique has shown great potential in revolutionizing the way we process business cards. Join us as we unravel the intricacies of this method and explore its future implications.
| Extraction Step | Description | Example |
|---|---|---|
| Optical Character Recognition (OCR) | Conversion of images of typed, handwritten, or printed text into machine-encoded text. | Image of “John Doe, CEO, XYZ Corp.” ➔ Text: “John Doe, CEO, XYZ Corp.” |
| Extracting and Classifying Phone Numbers | Identifying various phone number types based on prefixes. | Text: “Office: 123-456-7890” ➔ Classified as “Office Number” |
| Precision Address Extraction | Parsing the text for precise extraction of address details. | Text: “123 Maple St., Springfield, IL 62704” ➔ Extracted as: Street – “123 Maple St.”, City – “Springfield”, State – “IL”, Zip Code – “62704” |
| Connected Component Analysis | Identifying regions of interest for block processing based on pixel connectivity. | Image with “John” written closely, and “Doe” spaced apart ➔ Two components: “John” and “Doe” |
| Future Avenues for Business Card Extraction | Potential advancements in the extraction process. | Using Deep Learning for better contextual understanding. |
| Personal Details Extraction | Parsing text for personal names, designations, and organizations. | Text: “Dr. Jane Smith, Cardiologist, HealthCorp” ➔ Extracted as: Name – “Dr. Jane Smith”, Designation – “Cardiologist”, Organization – “HealthCorp” |
| Phone Numbers Extraction | Retrieving various phone numbers. | Text: “Fax: 987-654-3210” ➔ Extracted as “Fax Number” |
| Address Extraction | Parsing for the full address. | Text: “456 Elm St., Suite 7A” ➔ Extracted as “Address” |
| Zip Code Extraction | Isolating and extracting postal codes. | Text: “… Springfield, IL 62704” ➔ Extracted as “Zip Code – 62704” |
| City Extraction | Identifying and extracting city names. | Text: “… Springfield, IL …” ➔ Extracted as “City – Springfield” |
| State Extraction | Pinpointing and extracting state names or codes. | Text: “… Springfield, IL …” ➔ Extracted as “State – IL” |
| Country Extraction | Recognizing and extracting country names. | Text: “… USA” or “… United States of America” ➔ Extracted as “Country – USA” |