![]() However, the Stroke Width Transform (SWT), and OCR has difficulty in situations like blur, low contrast, and illumination change, since it is highly relies on the outcome from the edge detector. A notable work, which is Markov Random Field method (MRF), has been attracting much interest due to its simplicity and efficiency. We present a method called Markov Random Method for image operator that seeks to find the value of each image pixel, and demonstrate their use on the task of text detection in natural, which makes it fast and robust enough to eliminate the need for multi scale computation or scanning windows. ![]() Text detection and segmentation from handwritten images are useful in many applications. Detection of the texts from handwritten images is a challenging problem due to the multiple fonts, different sizes, various orientations and alignment, reflections, shadows, the complexity of image background. Text detection in handwritten image has gained widespread interests. The proposed system recognizes almost all characters of the input image. The character recognition step includes horizontal scanning, vertical scanning and recognition using Discrete Wavelet Transform (DWT). The performance analysis is done based on the contrast enhancement that is done to the adaptive contrast image. Also, a contrast enhancement has been done to the adaptive contrast image using three methods which are linear contrast enhancement, piecewise linear stretch and homomorphic filter. A strike removal method is also implemented to remove the strikes that are drawn over the text in the document images. Here, a character recognition technique is proposed for the degraded document images. The objective of document image analysis is to recognize the text in degraded document images and extract the intended information. While considering historical document analysis, old printed documents have a high occurrence of degraded characters, especially broken characters due to ink fading. ![]() ![]() Character recognition from noisy and degraded documents is still a challenging task. Today, there is a strong move towards digitization of old documents such as manuscripts so that their content can be preserved for future generations. But, due to many environmental factors, the poor quality of the materials used in their creation and improper handling cause them to suffer a high degree of degradation of various types. Libraries and archives around the world store large amount of old and historically important documents and manuscripts. So binarization is a technique that is used to remove the degradations. Character recognition becomes difficult if the document images are degraded. Optical character recognition (OCR) is the mechanical or electronic conversion of images of typewritten or printed text into machine-encoded text. This paper also provides the performance comparison of several existing methods proposed by researchers in extracting the text from an image. ![]() 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. ![]()
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