Abstract
In this paper, some morphological transformations are used to detect the unevenly illuminated background of text images characterized by poor lighting, and to acquire illumination normalized result. Based on morphologic Top-Hat transform, the uneven illumination normalization algorithm has been carried out, and typically verified by three procedures. The first procedure employs the information from opening based Top-Hat operator, which is a classical method. In order to optimize and perfect the classical Top-Hat transform, the second procedure, featuring the definition of multi direction illumination notion, utilizes opening by reconstruction and closing by reconstruction based on multi direction structuring elements. Finally, multi direction images are merged to the final even illumination image. The performance of the proposed algorithm is illustrated and verified through the processing of different ideal synthetic and camera collected images, with backgrounds characterized by poor lighting conditions.
Highlights
With the increasing development of digital image capturing devices, such as digital cameras, mobile phones and PDAs, their resolution is almost high enough to replace flatbed scanners
One of the most common techniques in normalization illumination image processing is histogram equalization and histogram specification, which are based on data statistical analysis, including global and local methods [11]
4.3 The illumination normalization algorithm based on Multi-direction SEs top-hat transforms
Summary
With the increasing development of digital image capturing devices, such as digital cameras, mobile phones and PDAs, their resolution is almost high enough to replace flatbed scanners. Optical character recognition (OCR) techniques and content-based image analysis techniques are receiving intensive attentions in recent years and text-mining tools are becoming essential [1]. Among all the contents in images, text information has inspired great interests
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