Vaibhav Kumar

Work place: Computer Science Department, Bharati Vidyapeeth’s College of Engineering, New Delhi, India



Research Interests: Applied computer science, Computer systems and computational processes, Computer Architecture and Organization, Theoretical Computer Science


Vaibhav Kumar: Under-graduate student pursuing Bachelor of Technology (B.Tech) at Bharati Vidyapeeth’s College of Engineering, New Delhi, India, major in Computer Science.

Author Articles
Text Extraction from Natural Scene Images using OpenCV and CNN

By Vaibhav Goel Vaibhav Kumar Amandeep Singh Jaggi Preeti Nagrath

DOI:, Pub. Date: 8 Sep. 2019

The influence of exponentially increasing camera-embedded smartphones all around the world has magnified the importance of computer vision tasks, and gives rise to a vast number of opportunities in the field. One of the major research areas in this field is the extraction of text embedded in natural scene images. Natural scene images are the images taken from a camera, where the background is random, and the variety of colors used in the image may be diverse. When text is present in such type of images, it is usually difficult for a machine to detect and extract this text due to a number of parameters. This paper presents a technique that uses a combination of the Open Source Computer Vision Library (OpenCV) and the Convolutional Neural Networks (CNN), to extract English text from images efficiently. The CNN model is based on a two-stage pipeline that uses a single neural network to directly detect the characters in the scene images. It eliminates the unnecessary intermediate steps that are present in the previous approaches to this task making them slower and inaccurate, thereby improving the time complexity and the performance of the algorithm.

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