Text recognition is a technique that recognizes text from the paper document in the desired format (such as .doc or .txt). The text recognition process involves several steps, including pre-processing, segmentation, feature extraction, classification, and post-processing. The preprocessing is performed as a binarized image to convert a grayscale image, and noise is reduced on the input image of the basic operation performed by removing the noise of the image signal. The segmentation phase is used to segment the image given online and segment each character of the segmentation line. Feature extraction is to compute the characteristics of the image document. This document describes techniques for converting the textual content of a paper document into a machine-readable format. This paper analyzes and compares the technical challenges, methods, and performance of text detection and recognition studies in color images. It summarizes the basic issues and lists the factors that should be considered when addressing them. The prior art is classified as step-by-step or integrated and highlights sub-problems including text localization, verification, segmentation and identification of text. This survey provides a basic comparison and analysis of the scope and challenges in the field of text recognition.

Classification, Datamining, Segmentation, Text recognition

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