Candidate Character Research Articles

Human Reading Knowledge Inspired Text Line Extraction

Text in images contains exact semantic information and the text knowledge can be utilized in many image cognition and understanding applications. The human reading habits can provide the clues of text line structure for text line extraction. In this paper, we propose a novel human reading knowledge inspired text line extraction method based on k-shortest paths global optimization. Firstly, the candidate character extraction is reformulated as Maximal Stable Extremal Region (MSER) algorithm on gray, red, blue, and green channels of the target images, and the extracted MSERs are fed into Convolutional Neural Network (CNN) to remove the noise components. Then, the directed graph is built upon the character component nodes with edges inspired by human reading sense. The directed graph can automatically construct the relationship to eliminate the disorder of candidate text components. The text line paths optimization is inspired by the human reading ability in planning of a text line path sequentially. Therefore, the text line extraction problem can be solved using the k-shortest paths optimization algorithm by taking advantage of the human reading sense structure of the directed graph. It can extract the text lines iteratively to avoid the exhaustive searching and obtain global optimized text line number. The proposed method achieves the f-measure of 0.820 and 0.812 on public ICDAR2011 and ICDAR2013 dataset, respectively. The experimental results demonstrate the effectiveness of the proposed human reading knowledge inspired text line extraction method in comparison with state-of-the-art methods This paper presents one human reading knowledge inspired text line extraction method, which approves that the human reading knowledge can benefit the text line extraction and image text discovery.

Method for unconstrained text detection in natural scene image

Text detection in natural scene images is an important prerequisite for many content‐based multimedia understanding applications. The authors present a simple and effective text detection method in natural scene image. Firstly, MSERs are extracted by the V‐MSER algorithm from channels of G, H, S, O1, and O2, as component candidates. Since text is composed of character candidates, the authors design an MRF model to exploit the relationship between characters. Secondly, in order to filter out non‐text components, they design a set of two‐layers filtering scheme: most of the non‐text components can be filtered by the first layer of the filtering scheme; the second layer filtering scheme is an AdaBoost classifier, which is trained by the features of compactness, horizontal variance and vertical variance, and aspect ratio. Then, only four simple features are adopted to generate component pairs. Finally, according to the orientation similarity of the component pairs, component pairs which have roughly the same orientation are merged into text lines. The proposed method is evaluated on two public datasets: ICDAR 2011 and MSRA‐TD500. It achieves 82.94 and 75% F‐measure, respectively. Especially, the experimental results, on their URMQ_LHASA‐TD220 dataset which contains 220 images for multi‐orientation and multi‐language text lines evaluation, show that the proposed method is general for detecting scene text lines in different languages.

Scene text segmentation using low variation extremal regions and sorting based character grouping

Extraction of textual information from natural scene images is a challenging task due to imaging conditions and diversity of text properties. Segmentation of scene text is important step in the pipeline that significantly affects the final recognition performance. In this paper I propose a new scene text segmentation method. Firstly, a novel approach for character candidates generation based on extremal regions (ERs) is introduced. Subpaths having low area variation are extracted from ER tree. Instead of using minimum variation criterion for selection of character candidates, position of ER in extracted subpath is used as criterion for that purpose. Each subpath is represented by one ER that is sent to SVM-based classification step. After that a novel method for character candidates grouping is used to discard non-character objects that are wrongly classified as characters. Proposed approach estimates vertical positions of the lines by sorting y coordinates of region centroids and checks spatial relation of adjacent regions in the line. This step enhances precision significantly and has lower computational complexity compared to hierarchical clustering methods. Finally, the last step is restoration of character ERs erroneously eliminated by SVM classifier where text layout properties are exploited to correct false negative classifications. Experimental results obtained on the ICDAR 2013 dataset show that the proposed character candidates generation method efficiently prunes repeating regions and achieves character recall rate superior to recently published ER based method. Proposed segmentation algorithm obtains competitive performance compared to state-of-the-art methods.

Chinese Text Detection and Recognition in Natural Scene Using HOG and SVM

It remains an important, yet challenging problem to detect and recognize text from natural scene images. Since earlier this year a number of methods have been proposed for reading text from scene images. But all of them are adapted for English alphabets, and not suitable for Chinese characters, which present unique challenges including highly versatile fonts, complex background and uneven illumination, a huge number of different characters, unconnected strokes within a character. In this paper we design a method tailored for reading Chinese characters from natural scenes. During the phase of character detection, we employ MSER (Maximally Stable Extremal Region) method to extract candidate characters, and then integrate extracted strokes through mathematical morphology computation. Based on the attributes of Chinese characters, we also lay down heuristic rules that distinguish text and non-text to screen the region of a candidate character. In this method, we describe the features of characters with HOG descriptor and accurately use SVM (Support Vector Machine) according to classification. And the positive region is the region covering the text. At the stage of text recognition, we use KNN (K-Nearest Neighbors) as the classifier. We test this method on 400 natural scene images containing Chinese characters collected from different sources. And the results have successfully validated the efficacy of our approach.

The Role of Organization Collage Student a Study Program to Build Leadership Character Candidate Chemistry Teacher

Abstract : The development of social media into today's style of life. The decline in the child's desire to follow communities a result of the use of social media affect the formation of character in children. Character education is a very good effort from all of people who develop education. Science briefing will children with good character is the responsibility of all of people involved in education for the future of dignity. Teachers is an asset in the world of education should have good prospects in education which is not a crisis of character. Organization is a place for prospective teachers to build character. This study aims to describe the role of student organization of study program in developing the leadership character of prospective educators of Chemistry Christian University of Indonesia. The focus in this research is the role of organization of student set of study program in shaping the leadership character of prospective educator. In this research, qualitative approach and case study research design is used to collect data by technique (1) in-depth interview, (2) nonparticipant observation, (3) documentation study. Data analysis techniques use data reduction, data presentation, verification or conclusion. The result of this research is the role of organization of student set of study program in shaping the leadership character of prospective educator by giving courage, integrity, good sense of humor and maturity which is 96.5%, 93,75%, 93,75% and 91, 25% on prospective educators. So, the students are expected to join the organization of student group of study program is effort done in shaping the leadership character of prospective educator of Chemistry Christian University of Indonesia. Keywords: character, chemistry, educators

Tracking Based Multi-Orientation Scene Text Detection: A Unified Framework With Dynamic Programming

There are a variety of grand challenges for multi-orientation text detection in scene videos, where the typical issues include skew distortion, low contrast, and arbitrary motion. Most conventional video text detection methods using individual frames have limited performance. In this paper, we propose a novel tracking based multi-orientation scene text detection method using multiple frames within a unified framework via dynamic programming. First, a multi-information fusion-based multi-orientation text detection method in each frame is proposed to extensively locate possible character candidates and extract text regions with multiple channels and scales. Second, an optimal tracking trajectory is learned and linked globally over consecutive frames by dynamic programming to finally refine the detection results with all detection, recognition, and prediction information. Moreover, the effectiveness of our proposed system is evaluated with the state-of-the-art performances on several public data sets of multi-orientation scene text images and videos, including MSRA-TD500, USTB-SV1K, and ICDAR 2015 Scene Videos.

A cascaded method for text detection in natural scene images

In this paper, a novel image operator is proposed to detect and locate text in scene images. To achieve a high recall of character detection, extremal regions are detected as character candidates. Two classifiers are trained to identify characters, and a recursive local search algorithm is proposed to extract characters that are wrongly identified by the classifiers. An efficient pruning method, which combines component trees and recognition results, is proposed to prune repeating components. A cascaded method combines text line entropy with a Convolutional Neural Network model. It is used to verify text candidates, which reduces the number of non-text regions. The proposed technique is test on three public datasets, i.e. ICDAR2011 dataset, ICDAR2013 dataset and ICDAR2015 dataset. The experimental results show that our approach achieves state-of-the-art performance.

Recognition-based online Kurdish character recognition using hidden Markov model and harmony search

In this paper a hidden Markov model and harmony search algorithms are combined for writer independent online Kurdish character recognition. The Markov model is integrated as an intermediate group classifier instead of a main character classifier/recognizer as in most of previous works. Markov model is used to classify each group of characters, according to their forms, into smaller sub groups based on common directional feature vector. This process reduced the processing time taken by the later recognition stage. The small number of candidate characters are then processed by harmony search recognizer. The harmony search recognizer uses a dominant and common movement pattern as a fitness function. The objective function is used to minimize the matching score according to the fitness function criteria and according to the least score for each segmented group of characters. Then, the system displays the generated word which has the lowest score from the generated character combinations. The system was tested on a dataset of 4500 words structured with 21,234 characters in different positions or forms (isolated, start, middle and end). The system scored 93.52% successful recognition rate with an average of 500ms. The system showed a high improvement in recognition rate when compared to similar systems that use HMM as its main recognizer.

Keyword spotting in handwritten chinese documents using semi-markov conditional random fields

This paper proposes a document indexing method for keyword spotting based on semi-Markov conditional random fields (semi-CRFs), which provide a theoretical framework for fusing the information of different contexts. The candidate segmentation-recognition lattice is first augmented based on the linguistic context to improve recognition results. For fast retrieval and to save storage space, the lattice is then purged by a forward-backward pruning procedure. In the reduced lattice, we estimate character similarity scores based on the semi-CRF model. The parameters of semi-CRF model are estimated using a binary classification objective, i.e., the cross-entropy (CE) to discriminate candidate characters in the lattice. To locate mis-recognized character instances in the lattice, we use confusing similar characters as proxies and search for proxy-characters in the index file. The proxy-character driven search can significantly improve the performance compared with our previous character-synchronous dynamic search (CSDS) method. Experimental results on the online handwriting database CASIA-OLHWDB justify the effectiveness of the proposed method.

Text detection in scene images based on exhaustive segmentation

In recent years, significant progress has been made in detecting text in scene images. However, most of state-of-the-art approaches can not work well when encountered blurred, low-resolution and small-sized texts. We consider many connected regions as candidates, which aim to capture character regions as many as possible. In this paper, we propose a novel method, which is based on exhaustive segmentation, to detect text in scene images. Firstly, we present a parallel structure to generate character candidate regions with the exhaustive segmentation of scene image. Secondly, a well-designed two-layer filtering method is used to filter out non-character candidate regions. Finally, at text line grouping stage, the edges of the fully connected graph of the remaining character candidate regions are cut by a support vector machine classifier. We use two public datasets, namely, ICDAR 2013 dataset and the Street View Text dataset to evaluate the performance of our method. Experimental results show that our method achieves excellent recall rate on these two public datasets, moreover, our method is robust to the blurred, low-resolution and small-sized texts.

Could scene context be beneficial for scene text detection?

Scene text detection and scene segmentation are meaningful tasks in the computer vision field. Could the semantic scene segmentation assist scene text detection in any degree? For example, can we expect the probability of a region being text is low if its surrounding segment, i.e., its context, is labeled as sky? In this paper, we have a positive answer by constructing a scene context-based text detection model. In this model, we use texton features and a fully-connected conditional random field (CRF) to estimate pixel-level scene class׳s probability to be considered as image׳s context feature. Meanwhile, maximally stable extremal regions (MSERs) are extracted, integrated and extended as image patches of character candidates. Then, each image patch is fed to a simple two-layer convolutional neural network (CNN) to automatically extract its character feature. The averaged context feature of the corresponding patch is considered as the patch׳s context feature. The character feature and context feature are fused as the input into a support vector machine for text/non-text determination. Finally, as a post-processing, neighboring text regions are grouped hierarchically. The performance evaluation on ICDAR2013 and SVT databases, as well as a preliminary evaluation on a patch-level database, proves that the scene context can improve the performance of scene text detection. Moreover, the comparative study with state-of-the-art methods shows the top-level performance of our method.

Evaluation of Chinese Calligraphy by Using DBSC Vectorization and ICP Algorithm

Chinese calligraphy is a charismatic ancient art form with high artistic value in Chinese culture. Virtual calligraphy learning system is a research hotspot in recent years. In such system, a judging mechanism for user’s practice result is quite important. Sometimes, user’s handwritten character is not that standard, the size and position are not fixed, and the whole character may be even askew, which brings difficulty for its evaluation. In this paper, we propose an approach by using DBSCs (disk B-spline curves) vectorization and ICP (iterative closest point) algorithm, which cannot only evaluate a calligraphic character without knowing what it is, but also deal with the above problems commendably. Firstly we find the promising candidate characters from the database according to the angular difference relations as quickly as possible. Then we check these vectorized candidates by using ICP algorithm based upon the skeleton, hence finding out the best matching character. Finally a comprehensive evaluation involving global (the whole character) and local (strokes) similarities is implemented, and a final composited evaluation score can be worked out.

Scene text localization using edge analysis and feature pool

Due to the rapid development of machine learning and data mining in nowadays, how to acquire information from images becomes more and more important. The direct information of an image is the texts inside. However, detecting such texts in images is always a challenging problem in computer vision area.Edge is one of the most important clues in scene character detection task. However, many edge based text detection methods usually had trouble with sticky edges and did not fully utilize characteristic of texts. In this paper, we proposed a method for detecting and localizing texts in natural scene images, by edge recombining, edge filtering and multi-channel processing. In order to segment texts from backgrounds, edges are firstly over-segmented into edge segments during edge analysis. These edge segments are then recombined to candidate characters and an edge filter is used to filter out most of background edges. The left candidate character edges are linked up to candidate text lines. We use two different classifiers to filter out non-text lines. To classify more accurately, extracted edge-based and region-based features are firstly stored in feature pools. Then we use liner SVM to select the most effective features from the feature pool to train classifiers. Finally, multi-channel is used to ensure the recall and a modified non-maximal suppress is applied to eliminate duplicate results. Experimental results on the ICDAR 2011 competition dataset and SVT database demonstrate the effectiveness of our method.

Detecting text in natural scene images with conditional clustering and convolution neural network

We present a robust method of detecting text in natural scenes. The work consists of four parts. First, automatically partition the images into different layers based on conditional clustering. The clustering operates in two sequential ways. One has a constrained clustering center and conditional determined cluster numbers, which generate small-size subregions. The other has fixed cluster numbers, which generate full-size subregions. After the clustering, we obtain a bunch of connected components (CCs) in each subregion. In the second step, the convolutional neural network (CNN) is used to classify those CCs to character components or noncharacter ones. The output score of the CNN can be transferred to the postprobability of characters. Then we group the candidate characters into text strings based on the probability and location. Finally, we use a verification step. We choose a multichannel strategy to evaluate the performance on the public datasets: ICDAR2011 and ICDAR2013. The experimental results demonstrate that our algorithm achieves a superior performance compared with the state-of-the-art text detection algorithms.

An intelligent character recognition method to filter spam images on cloud

Cloud storage has become an important way for data sharing in recent years. Data protection for data owner and harmful data filtering for data recipients are two non-negligible problems in cloud storage. Illegal or unsuitable messages on cloud have a negative impact on minors and they are easily converted into images to avoid text-based filtering. To detect the spam image with the embedded harmful messages on cloud, soft computing methods are required for intelligent character recognition. HOG, proposed by Dalal and Triggs, has been demonstrated so far to be one of the best features for intelligent character recognition. A pre-defined sliding window is always used for the generation of candidate character images when HOG is applied to recognize the whole word. However, due to the difference in character sizes, the pre-defined window cannot exactly match with each character. Variations on scale and translation usually occur in the character image to be recognized, which have a great influence on the performance of intelligent character recognition. Aiming to solve this problem, STRHOG, an extended version of HOG, is proposed in this paper. Experiments on two public datasets and one our dataset have shown encouraging results for our work. The improved intelligent character recognition is helpful for filtering spam images on cloud. To make a fair comparison with other methods, nearest neighbor classifier is used for the intelligent character recognition. It is expected that the performance should be further improved by using better classifiers such as fuzzy neural network.

A robust hybrid method for text detection in natural scenes by learning-based partial differential equations

Learning-based partial differential equations (PDEs), which combine fundamental differential invariants into a non-linear regressor, have been successfully applied to several computer vision tasks. In this paper, we present a robust hybrid method that uses learning-based PDEs for detecting texts from natural scene images. Our method consists of both top-down and bottom-up processing, which are loosely coupled. We first use learning-based PDEs to produce a text confidence map. Text region candidates are then detected from the map by local binarization and connected component clustering. In each text region candidate, character candidates are detected based on their color similarity and then grouped into text candidates by simple rules. Finally, we adopt a two-level classification scheme to remove the non-text candidates. Our method has a flexible structure, where the latter part can be replaced with any connected component based methods to further improve the detection accuracy. Experimental results on public benchmark databases, ICDAR and SVT, demonstrate the superiority and robustness of our hybrid approach.

Multi-scaled license plate detection based on the label-moveable maximal MSER clique

In this paper, we consider a robust vehicle license plate detection problem for intelligent transportation systems in the presence of various illumination situations. We propose a robust and fast multi-scaled license plate detection and location algorithm, which exploits a Label-Moveable Maximal MSER clique. Specifically, first, we extract the candidate character regions using the Maximally Stable Extremal Region (MSER) features. Second, we divide each candidate character region into four types and extract the suspected initial node (the top-left character) based on its neighbor MSER distribution characteristic. Third, we label each candidate character region to accomplish license detection and location based on the detected suspected initial node and the corresponding label-moveable maximal MSER clique. The robust of license plate detection, the accuracy of character labeling for license location, and the improvement of calculation efficiency are evaluated via the real data.

Scene text recognition using a Hough forest implicit shape model and semi-Markov conditional random fields

Most of the scene text recognition methods utilize character models only in the character recognition phase, the last stage of the process. In former phases such as text detection, only abstracted features of text regions are used, which might cause loss of information. In this paper, we propose a novel scene text recognition method which fully utilizes model of target characters throughout the process. Each of the target character set is modeled with a part-based object model called implicit shape model (ISM) to achieve robustness for the partial degradation of characters. Towards this end, we trained a Hough forest which localizes and aggregates character parts to detect character candidates in the image. The detected character candidates are verified by organizing the most plausible text lines in a semi-Markov conditional random field (semi-CRF) framework. As concrete character models are utilized throughout the process, even extremely deformed texts are detected and recognized.

A robust approach for text detection from natural scene images

This paper presents a robust text detection approach based on color-enhanced contrasting extremal region (CER) and neural networks. Given a color natural scene image, six component-trees are built from its grayscale image, hue and saturation channel images in a perception-based illumination invariant color space, and their inverted images, respectively. From each component-tree, color-enhanced CERs are extracted as character candidates. By using a “divide-and-conquer” strategy, each candidate image patch is labeled reliably by rules as one of five types, namely, Long, Thin, Fill, Square-large and Square-small, and classified as text or non-text by a corresponding neural network, which is trained by an ambiguity-free learning strategy. After pruning unambiguous non-text components, repeating components in each component-tree are pruned further. Remaining components are then grouped into candidate text-lines and verified by another set of neural networks. Finally, results from six component-trees are combined, and a post-processing step is used to recover lost characters. Our proposed method achieves superior performance on both ICDAR-2011 and ICDAR-2013 “Reading Text in Scene Images” test sets.

Candidate character traits in presidential elections

1. Candidate Character Traits in Presidential Elections 2. Conceptualizing and Measuring Candidate Character Traits 3. Voter Perceptions of Candidate Character Traits 4. The Impact of Candidate Character Trait Perceptions on the Vote 5. The Formation of Candidate Character Trait Perceptions 6. Candidate Character Traits in the 2012 Presidential Election 7. Media Consumption, Character Trait Perceptions, and Voting 8. May the Best Person Win