OCR Technology Overview and Implementation Steps for Card Number Recognition

OCR (Optical Character Recognition) converts printed or handwritten text from scanned images into machine‑readable data, enabling digitization of documents, IDs, cards, and more.

Typical application scenarios include license plates, identity cards, bank cards, passports, business cards, and captcha images.

The article outlines a five‑step process for recognizing numeric information on bank/credit cards: target extraction, preprocessing, character localization, digit matching, and format validation.

Target Extraction: Define a detection region, correct perspective, and isolate the card area using edge detection and line filtering to obtain four precise edges.

Preprocessing: Depending on card type, apply binary thresholding for printed cards or morphological gradient (dilation followed by erosion) for embossed cards to enhance character contrast.

Character Localization: Perform vertical projection to find row boundaries, then horizontal projection to locate individual character columns, using density analysis and predefined character width ranges.

Digit Matching: Generate template images for digits 0‑9 and match each detected character region against these templates, optionally using a confidence threshold to accept matches and caching results for robustness.

Format Validation: Apply the Luhn algorithm to verify the checksum of the recognized card number, ensuring the result meets the required format before presenting it to the user.

The workflow demonstrates how image processing, morphological operations, projection analysis, template matching, and checksum validation combine to achieve reliable OCR for numeric card data.