Renhong Cheng

Bio: Renhong Cheng is an academic researcher from Nankai University. The author has contributed to research in topics: Image segmentation & Conditional random field. The author has an hindex of 2, co-authored 2 publications receiving 13 citations.

Research on the Text Detection and Extraction from Complex Images

Abstract: The text appears in the images is important for fully understanding the images. The number of digital images and digital videos has increased tremendously. Although there are many methods have been proposed over the past years for the text extraction from natural scene images, the text detection and extraction from born-digital images are still a challenge. In this paper, we describe existing methods key ideas and try to summarize their advantages and disadvantages. We try to find a new way which can Comprehensive utilize existing methods to detect and extract text from born-digital image.

Research on Born-Digital Image Text Extraction Based on Conditional Random Field

Abstract: Born-digital images are generated directly with the computer, the text in the images is important for fully understanding the images. Although there are many methods having been proposed over the past years for text extraction from natural scene images, text detection and extraction from born-digital images are still a challenge. This paper proposed an algorithm of text extraction from born-digital images based on conditional random field (CRF). CRF model not only considers unary component properties and binary contextual component relationships, but also learn parameter s with supervised. This paper combines features and relationships within the CRF framework and the experiment results show that this algorithm can extract text effectively from the born-digital images.

A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video

Abstract: The present paper presents one of the efficient approaches toward multilingual text detection for video indexing. In this paper, we propose a method for detecting textlocated in varying and complex background in images/video. The present approach comprises four stages: In the first stage, combination of wavelet transform and Gabor filter is applied. By applying single level 2D wavelet decomposition with Gabor Filter, the intrinsic features comprising sharpen edges and texture features of an input image are obtained. In the second stage, the resultant Gabor image is classified using k-means clustering algorithm. In the third stage, morphological operations are performed on clustered pixels. Then a concept of linked list approach is used to build a true textline sequence of connected components. In the final stage, wavelet entropy of an input image is measured by signifying the complexity of unsteady signals corresponding to the position of textline sequence of connected components in leading to determine the true text region of an input image. The performance of the approach is exhibited by presenting promising experimental results for 101 video images, standard ICDAR 2003 Scene Trial Test dataset, ICDAR 2013 dataset and on our own collected South Indian Language dataset.

Text detection and localization in scene images: a broad review

Abstract: Nowadays, text detection and localization have gained much popularity in the field of text analysis systems as they pave the way for the number of real-time based applications like mobile transliteration technologies, assistive methods for visually impaired persons, etc. Text detection and localization techniques are used to find the position of text area in the image.This paper intends to present a broad review in this field as five-fold: (1) comparison of document images with scene images and applications of natural scene images, (2) significant and up-to-date traditional machine learning and deep learning-based approaches for the text detection and localization for different languages, (3) various publicly available benchmarked datasets, (4) comparative analysis for other benchmarked datasets and, (5) related challenges and future scope on the field. The paper summarises some of the potential ways in this field, which can serve as a useful reference for the researchers for future exploration of the area.

Survey on Text Detection, Segmentation and Recognition from a Natural Scene Images

Abstract: Detecting text from an image is an important prerequisite for the content based image analysis process. To understand the contents of an image or the valuable information, there is need of analyzing the text appears in it. Various methods have been proposed over past years for text detection and extraction from different types of images, like scene image, born digital image and document image. In this paper, we describe the existing methods of text detection, text segmentation and character recognition from natural scene images with their features, advantages and disadvantages. General Terms Pattern Recognition

Layout and Text Extraction from Document Images using Neural Networks

Abstract: This paper attempts to provide a new perspective for efficient text extraction techniques by including the layout information of the document images along with the text. The study proposes a novel approach of segmenting the image into smaller images based on its meta-data knowledge and then applying functions for recognition of text from the smaller images. Due to a lack of layout information of the text, poor results are generated during text searching by office automation tools. With this restriction usage of the text for various environments becomes limited and usage of extracted text may not be done effectively. The study proposes a technique to understand the structural and functional layout of the document image and using this knowledge to develop a better model. To verify the point of view, additional intelligence is attached to the data, making it capable to be used in varied environments. With this added quality, the new proposed system can extract and identify text or group of text into different entities within the document which the previous systems could not achieve. The proposal can be beneficial particularly for the development of various document processing tools.

Investigating the precision of Web image search engines for popular and less popular entities

Abstract: Image search is the second most frequently used search service on the Web. However, there are very few studies investigating any aspect of it. In this study, we investigate the precision of Web image search engines of Google and Bing for popular and less popular entities using text-based queries. Furthermore, we investigate four additional aspects of Web image search engines that have not been studied before. We used 60 different queries in total from three different domains for popular and less popular categories. We examined the relevancy of the top 100 images for each query. Our results indicate that image search is a solved problem for popular entities. They deliver 97% precision on the average for popular entities. However, precision values are much lower for less popular entities. For the top 100 results, average precision is 48% for Google and 33% for Bing. The most important problem seems to be the worst cases in which the precision can be less than 10%. The results show that significant improveme...