Binarizing Business Card Images for Mobile Devices

Abstract— Business card images are of multiple natures as these often contain graphics, pictures and texts of various fonts and sizes both in background and foreground. So, the conventional binarization techniques designed for document images can not be directly applied on mobile devices. In this paper, we have presented a fast binarization technique for camera captured business card images. A card image is split into small blocks. Some of these blocks are classified as part of the background based on intensity variance. Then the non-text regions are eliminated and the text ones are skew corrected and binarized using a simple yet adaptive technique. Experiment shows that the technique is fast, efficient and applicable for the mobile devices.

Keywords— Binarization, Business Card Reader, Text Extraction, Skew Correction I. INTRODUCTION With the pervasive availability of low cost portable imaging devices, digital camera has become so popular that majority of the mobile devices such as cell-phones and Portable Digital Assistants (PDA) have inbuilt digital camera. The resolution of these cameras is getting increased day by day. The computing power and primary memory of the mobile devices are also gradually going higher. So, the idea of image processing and analysis is no more limited to desktop computation. Researchers have paid significant attention towards developing Optical Character Recognition (OCR) systems for document images on mobile devices. High resolution flatbed scanners are used for desktop processing of document images. Scanner captured document images hardly suffer from blur, shadow, skew and perspective distortion whereas these are very common for camera captured documents. On the other hand, mobile devices are portable and so more useful than scanners for document processing, particularly for capturing and processing any arbitrary documents such as thick books, fragile documents like old historical manuscripts, scene texts, caption texts, graphic texts, etc. Business Card Reader (BCR) for mobile devices is such a useful application of camera captured document image processing. With the development of an efficient BCR system, the information of the acquired business card images can be directly populated to the contact profile of the mobile devices. Thus, the business card management will be of great ease than ever before. None would have to carry the business card album nor have to type the information of the cards to populate into the handheld devices.
One of the major challenges of designing such a system is the binarization of business card images. Business card images often have complex background and texts of multiple natures. These images may contain logo, picture, texts of different fonts and various font sizes, graphic background, etc. Therefore, binarization can not be done straight forward and we found that neither global nor locally adaptive binarization techniques [1-4] can be applied for such images. Majority of the conventional binarization techniques are for scanned documents and some are for camera captured document images too. Seeger et al. [5] has presented an algorithm based on Background Surface Thresholding (BST) for binarizing camera captured document images. At first, the texts are eliminated from the document image and then the background is interpolated. They compute a threshold with the help of this interpolated background image and the original image, and binarize the document using it. Kim et al. [6] has presented an adaptive multi-window binarization method for document images acquired by camera. Information from the global trend as well as the local detail is used to determine the local threshold by applying multiple windows varying in sizes. Stroke neighborhood enhancement based document image binarization method is found in [7]. The foreground pixels are marked and the strokes are enhanced based on their neighborhood information. Then the enhanced image is binarized by incorporating contrast enhancing function and the smooth function. Segmentation based binarization approach is found in [8]. Literatures suggest that most of the binarization methods are for document images. They may not be directly applicable for binarizing business card images. In this paper, we have presented a novel technique for binarizing business card images, designed in our work towards developing an efficient BCR system for mobile devices. Experiments show that the technique has low computational overhead, and is fast and efficie

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