However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. Several techniques and methods have been presented for IE from unstructured data. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Effective use of these unstructured big data is a laborious and tedious task. This paper also provides the performance comparison of several existing methods proposed by researchers in extracting the text from an image.ĭuring the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation.

This article discusses various schemes proposed earlier for extracting the text from an image. All these techniques have their benefits and restrictions. The proposed methods were based on morphological operators, wavelet transform, artificial neural network,skeletonization operation,edge detection algorithm, histogram technique etc. Due to rapid growth of available multimedia documents and growing requirement for information, identification, indexing and retrieval, many researches have been done on text extraction in images.Several techniques have been developed for extracting the text from an image.

These text characters are difficult to be detected and recognized due to their deviation of size, font, style, orientation, alignment, contrast, complex colored, textured background. Text extraction involves detection, localization, tracking, binarization, extraction, enhancement and recognition of the text from the given image. Text Extraction plays a major role in finding vital and valuable information.

Text extractor from image