Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.

A GA based hierarchical feature selection approach for handwritten word recognition

Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naive Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.

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Handwritten Digit Recognition Using Machine Learning Algorithms

Due to the cursive nature, segmentation of handwritten Bangla words into characters and also recognition of the same sometimes become a very challenging problem to the researchers. Presence of comparatively large character set along with modifiers, ascendants, descendants, and compound characters makes the segmentation task more complex. As holistic method avoids such character-level segmentation, it is generally useful for the recognition of words written in any such complex scripts. In the present work, a holistic handwritten word recognition method is developed using a feature descriptor, designed by combining different Elliptical, Tetragonal and Vertical pixel density histogram-based features. Recognition process is carried out separately using two classifiers, namely multi-layer perceptron (MLP) and support vector machine (SVM). For evaluation of the proposed method, a database of 18,000 handwritten Bangla word images, having 120 word classes, is prepared. The proposed system performs comparatively better with SVM than MLP for the prepared dataset. It has achieved 83.64% accuracy at best case and 79.38% accuracy on an average using fivefold cross-validation. The current method has also outperformed some recently reported holistic word recognition technique tested on the developed dataset. In addition to that the database, prepared in this work, is made freely available to fill the absence of a publicly available standard database for holistic Bangla word recognition.

Off-line Bangla handwritten word recognition: a holistic approach

Nowadays, feature selection is considered as a de facto standard in the field of pattern recognition where high-dimensional feature attributes are used. The main purpose of any feature selection algorithm is to reduce the dimensionality of the input feature vector while improving the classification ability. Here, a Memetic Algorithm (MA)-based wrapper–filter feature selection method is applied for the recognition of handwritten word images in segmentation-free approach. In this context, two state-of-the-art feature vectors describing texture and shape of the word images, respectively, are considered for feature dimension reduction. Experimentation is conducted on handwritten Bangla word samples comprising 50 popular city names of West Bengal, a state of India. Final results confirm that for the said recognition problem, subset of features selected by MA produces increased recognition accuracy than the individual feature vector or their combination when applied entirely.

Feature Selection for Handwritten Word Recognition Using Memetic Algorithm

Document image binarization classifies each pixel in an input document image as either foreground or background under the assumption that the document is pseudo binary in nature. However, noise introduced during acquisition or due to aging or handling of the document can make binarization a challenging task. This paper presents a novel game theory inspired binarization technique for degraded document images. A two-player, non-zero-sum, non-cooperative game is designed at the pixel level to extract the local information, which is then fed to a $K$ -means algorithm to classify a pixel as foreground or background. We also present a preprocessing step that is performed to eliminate the intensity variation that often appears in the background and a post-processing step to refine the results. The method is tested on seven publicly available datasets, namely, DIBCO 2009–14 and 2016. The experimental results show that game theory inspired binarization outperforms competing state-of-the-art methods in most cases.

GiB : A ${G}$ ame Theory ${I}$ nspired ${B}$ inarization Technique for Degraded Document Images

The holistic approaches for handwritten word recognition treat the words as single, indivisible entity and attempt to recognize words from their overall shape. In the present work, a novel technique to recognize handwritten Bangla word is proposed. Histograms of Oriented Gradients (HOG) are used as the feature set to represent each word sample at the feature space and a neural network based classifier is applied to classify the word images. On the basis of the HOG feature set, the performance achieved by the technique on a small dataset is quite satisfactory.

Handwritten Bangla Word Recognition Using HOG Descriptor

Holistic word recognition is the current trend for handwritten word recognition. The holistic paradigm in handwritten word recognition considers a word as a single, indivisible entity and attempts to recognise words from their overall shape unlike recognising the individual characters comprising the word. In the present work, concentric rectangles and convex hull-based features are designed in order to classify word images belonging to different classes. For the evaluation of the current technique, 2,754 handwritten Bangla word samples are collected from different sources. A neural network-based classifier is chosen on the basis of the performances of different classifiers and some statistical tests. The recognition performance of the technique is evaluated using a three-fold cross-validation method. From the experimental results, it is observed that the proposed technique correctly recognises 84.74% word images in best case.

Md. Galib Roushan

Papers

Handwritten Bangla Word Recognition Using HOG Descriptor

A holistic word recognition technique for handwritten Bangla words