Separation of Text Components from Complex Colored Images

G Gayathri Devi,C.P Sumathi

doi:10.19026/rjaset.8.1005

Abstract

The objective of this study is to project a new methodology for text separation in an image. Gamma Correction Method is applied as a preprocess technique to suppress non text regions and retain text regions. Text Segmentation is achieved by applying Positional Connected Component Labeling, Text Region Extraction, Text Line Separation, Separation of Touching Text and Separation of Text Components algorithms. At last, the details of each word's and the line's starting text component position are stored in a text file. Experiments are conducted on various images from the datasets collected and tagged by the ICDAR Robust Reading Dataset Collection Team. It is observed that the proposed method has an average recall rate of 97.5% on separation of text components in an image.

Highlights

Rapid development of digital technology has resulted in digitization of all categories of materials
Recognition of the text data in document images depends on the efficient separation of text
The study presents a new algorithm for the separation of text region information in an image

Summary

Introduction

Rapid development of digital technology has resulted in digitization of all categories of materials. Text data present in images and video contain useful information for detection of vehicle license plate, name plates, keyword based image search, content based retrieval, text based video indexing, video content analysis, document retrieving, address block location etc. Recognition of the text data in document images depends on the efficient separation of text. Many methods have been proposed for text separation in images and videos. It is not easy to describe a unified method as there are low-contrast or complex images, text with variations in font size, style, color, orientation and alignment etc

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Methods

Conclusion