A GA based hierarchical feature selection approach for handwritten word recognition

Malakar, Samir; Ghosh, Manosij; Bhowmik, Showmik; Sarkar, Ram; Nasipuri, Mita

doi:10.1007/s00521-018-3937-8

Cited by 140 publications

(45 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the other sets of values in consideration, these values provide better results in pre-processing, as also depicted pictorially in Figure 3. Finally, the contrast-normalized image's intensity value of each pixel (i.e., ( , )) is given by Equation (3). Figure 4 shows the contrast-normalized output, corresponding to the input word image.…”

Section: Contrast Normalizationmentioning

confidence: 99%

“…Such efforts of obtaining the underlying machine-readable text from handwritten documents have opened up a new research domain known as handwritten text recognition (HTR). Despite some notable success in HTR as found in the literature [1][2][3], many uncertain problems associated with HTR remain unresolved. These problems are mainly related to variations in writing styles among different individuals, as well as that within a single individual, due to changes in mood, age, time, environment, or situation, etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Kundu

Malakar²,

Geem

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.

show abstract

Section: Contrast Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Kundu

Malakar²,

Geem

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…This ultimately stretches the dimension of feature set and brings down the overall accuracy in making predictions. Feature selection (FS)-an initiative to optimize feature dimensions undertaken by various researchers-aims in extricating redundant/irrelevant features that do not make any significant contribution in the overall prediction process [7,8]. FS is a useful way for substantial reduction of the size of original-feature vectors used to predict target facial emotions expressed by humans.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

Saha¹,

Ghosh

et al. 2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

show abstract

“…Depending on the criteria of evaluation. FS is broadly divided into three separate categories namely filter [6][7][8], wrapper [9][10][11] and embedded [12][13][14] models. Filter methods use the statistical characteristics and intrinsic properties of features to evaluate candidate solutions, whereas wrapper methods take help of a learning algorithm (a classifier) to evaluate the solutions at every iteration.…”

Section: Introductionmentioning

confidence: 99%

“…Flowchart of CGA model consisting of two different segments, namely EGA and Coalition game interacting with each other. dataset[11] consists of six classes, namely Boxing, Hand-clapping, Hand-waving, Jogging, Running, Walking. The dataset consists of 599 videos which are equally divided among the classes (100 videos each) except Hand-clapping which consists of 99 videos.…”

mentioning

confidence: 99%

CGA: a new feature selection model for visual human action recognition

Guha

Khan

Singh

et al. 2020

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel Feature Selection (FS) model called Cooperative Genetic Algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets such as Weizmann, KTH, UCF11, HMDB51 and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering nearly 50 to 60% of the original feature vector.

show abstract

A GA based hierarchical feature selection approach for handwritten word recognition

Cited by 140 publications

References 40 publications

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

CGA: a new feature selection model for visual human action recognition

Contact Info

Product

Resources

About