A Novel Text Recognition Scheme using Classification Assisted Digital Image Processing Strategy

In the industry of Digital Image Processing, Text Recognition is an important task due to the significance of many classical records available today is in the format of paper record. The main objective of such text recognition schemes are transforming the textual records from hard copy to the system oriented records, in which it will be easier to maintain it into the database or any server entities in easy way. This paper is intended to design a novel text recognition using digital images taken from any camera with a set of different pixel densities. In this paper, a Logical Text Classification Strategy (LTCS) is introduced to perform an effective text recognition process using digital images. The proposed LTCS process the input image based on certain characteristics such as: Image Pre-Processing, Segmenting the Image, Extracting the Features, Classification Principle and the Image Post-Processing. These are all the different steps involved in the processing of proposed text recognition approach. For information indexing and search applications, characters in the document implanted in images portray a valuable source of data. Moreover, owing to the unique dimensions, gray - scale true values and background clutter, these text words/characters are harder to identify and recognize. This paper analyses approaches for designing an integrated implementation tool for classifying and recognizing text hidden in digital images that has any grey scale value. In this process two main considerations are important, such as text identification and text recognition, both of these empirical image processing techniques and quantitative classification approaches are investigated in this paper. The resulting section shows the processing efficiency, time required to process the digital image and character recognition efficiency in clear manner using graphical representations.