Visiting cards hold crucial contact information, yet manually digitizing these details is time-consuming and error-prone, especially in high-volume scenarios. Existing methods utilizing optical character recognition (OCR) alone often struggle with the accuracy needed for effective data classification. This study addresses these limitations by implementing a system that leverages OCR, regular expressions (Regex), and Named Entity Recognition (NER) to automate visiting card data extraction and classification. The proposed system employs EasyOCR for initial text extraction, followed by Regex for structured entities (such as phone numbers and emails) and NER using a bidirectional LSTM (BiLSTM) model to classify unstructured information like names and addresses. This combination enhances both precision and adaptability, achieving an accuracy of 99% on a custom-annotated dataset. Results indicate that while Regex efficiently extracts structured data, integrating NER markedly improves the differentiation of complex entities, reducing misclassifications between names and organizations. The system’s robust performance makes it suitable for various industries needing efficient contact data management. Future applications include potential integration with customer relation management (CRM) systems and the automation of database entry in high-traffic business environments, aiming to enhance operational efficiency across sectors.