Page-to-Word Extraction from Unconstrained Handwritten Document Images

Singh, Pawan Kumar; Chowdhury, Shyama Prosad; Sinha, Shubham; Eum, Sungmin; Sarkar, Ram

doi:10.1007/978-981-10-2035-3_53

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Figure 4 shows samples of handwritten text blocks in 12 Indian scripts. In a similar manner, the words and then the test-lines are also extracted from the input document pages employing the techniques described in [46] and [47]…”

Section: Preparation Of Handwritten Indic Script Databasementioning

confidence: 99%

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

Guha

Ghosh

Singh

et al. 2021

Complex Intell. Syst.

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In any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS.

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Section: Preparation Of Handwritten Indic Script Databasementioning

confidence: 99%

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

Guha

Ghosh

Singh

et al. 2021

Complex Intell. Syst.

Self Cite

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“…Figure shows handwritten samples of Indic scripts at block‐level taken from our database. Text lines and then text words are also extracted from the input document pages using the techniques described by Khandelwal et al () and Pawan Kumar Singh, Chowdhury, et al (), respectively. Text‐line and word samples from our database written in 12 official Indian scripts are illustrated in Figures and , respectively.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A clustering‐based feature selection framework for handwritten Indic script classification

et al. 2019

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In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well.

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“…The scanned images may contain noisy pixels which are removed by applying Gaussian filter [33]. The text words are automatically extracted from the handwritten documents by using a page-to-word segmentation algorithm described in [44]. A sample snapshot of word images written in 12 different scripts is shown in Figure 7.…”

Section: Preparation Of Databasementioning

confidence: 99%

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

et al. 2018

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Script identification is an essential step in document image processing especially when the environment is multi-script/multilingual. Till date researchers have developed several methods for the said problem. For this kind of complex pattern recognition problem, it is always difficult to decide which classifier would be the best choice. Moreover, it is also true that different classifiers offer complementary information about the patterns to be classified. Therefore, combining classifiers, in an intelligent way, can be beneficial compared to using any single classifier. Keeping these facts in mind, in this paper, information provided by one shape based and two texture based features are combined using classifier combination techniques for script recognition (word-level) purpose from the handwritten document images. CMATERdb8.4.1 contains 7200 handwritten word samples belonging to 12 Indic scripts (600 per script) and the database is made freely available at https://code.google.com/p/cmaterdb/. The word samples from the mentioned database are classified based on the confidence scores provided by Multi-Layer Perceptron (MLP) classifier. Major classifier combination techniques including majority voting, Borda count, sum rule, product rule, max rule, Dempster-Shafer (DS) rule of combination and secondary classifiers are evaluated for this pattern recognition problem. Maximum accuracy of 98.45% is achieved with an improvement of 7% over the best performing individual classifier being reported on the validation set.

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Page-to-Word Extraction from Unconstrained Handwritten Document Images

Cited by 7 publications

References 8 publications

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

A clustering‐based feature selection framework for handwritten Indic script classification

A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition

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