Text extraction from images using gamma correction method and different text extraction methods — A comparative analysis

Abstract

Text Extraction plays a major role in finding vital and valuable information. Due to rapid growth of available multimedia documents and growing requirement for information, identification, indexing and retrieval, many researchers have been done on text extraction in images. The text characters are difficult to be detected and recognized due to their deviation of size, font, style, orientation, alignment, contrast, complex colored, textured background. Several techniques have been developed for extracting the text from an image. The methods were based on morphological operators, wavelet transform, artificial neural network, skeletonization operation, edge detection algorithm, histogram technique etc. This article provides the performance comparison of several algorithms, on the standard ICDAR dataset proposed by researchers in extracting the text from an image. The experimental result shows the efficiency of gamma correction method is better than the result of other well-known existing methods.