Off-line Bangla handwritten word recognition: a holistic approach

Bhowmik, Showmik; Malakar, Samir; Sarkar, Ram; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

doi:10.1007/s00521-018-3389-1

Cited by 47 publications

(23 citation statements)

References 46 publications

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“…So we prepare an in-house dataset containing 20 different circuit components which are listed in Table 1. We collect 150 samples for each circuit component that are drawn by different individuals who happen to be engineering students, faculty members and research scholars in a pre-formatted datasheet similar to the works [7,21]. In total, we make a dataset containing 3000 sample images of 20 different circuit components (150 per circuit component).…”

Section: Database Preparationmentioning

confidence: 99%

Offline hand-drawn circuit component recognition using texture and shape-based features

Roy

Bhattacharya

Sarkar

et al. 2020

Multimed Tools Appl

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Circuit diagram is the very foundation of electrical and electronic sciences. A circuit diagram consists of various symbols called circuit components that specify the functionality of that circuit. Every day-today gadgets that we use are made up with a number of electrical/electronic circuits to play out their particular tasks. Till date circuit designers have to physically enter all data from the hand-drawn circuits into computers, and this procedure requires some investment in terms of time and carries mistakes with high likelihood. To this end, in this paper, we propose a method that relaxes this constraint by introducing a method for recognition of hand-drawn electrical and electronic circuit components, with both analog and digital components included. In the proposed method, the pre-processed images of circuit components are used for training and testing a recognition model using a feature set consisting of a texture based feature descriptor, called histogram of oriented gradients (HOG), and shape based features that include centroid distance, tangent angle, and chain code histogram. In addition, the texture based feature, being large in number compared to others is optimized using a feature selection algorithm called ReliefF. Classification of components is done by using sequential minimal optimization (SMO) classifier. The proposed method has been evaluated on a dataset of 20 different circuit components with 150 samples in each class. The experimental outcome shows that the proposed approach provides average 93.83% accuracy on the present database. We also compare our method with some of the state-of-the-art methods and we see that our method outperforms these methods.

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Section: Database Preparationmentioning

confidence: 99%

Offline hand-drawn circuit component recognition using texture and shape-based features

Roy

Bhattacharya

Sarkar

et al. 2020

Multimed Tools Appl

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“…Though Chinese and Japanese scripts have larger character classes, the characters in the text are written in a well‐separated manner. In contrast, the texts written in Indic scripts such as Devanagari, Bangla, and Odia, are cursive, which makes the recognition process more tedious [4].…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, plenty of works have been reported for handwritten word recognition in Chinese/Japanese [5–7], Arabic [3, 8–10], and Roman scripts [11, 12]. However, in contrast, limited works [4, 13, 14] have been published for handwritten word recognition in Bangla script. Bangla is one of the most popular official languages of India and also the national language of Bangladesh.…”

Section: Introductionmentioning

confidence: 99%

H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition

Das

Nayak

Dash

et al. 2020

IET Image Processing

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“…Однако в настоящее время не существует универсальной системы, способной автоматически производить сегментацию рукописного текста на символы, что связано с высокой вариативностью написания текстов. В процессе письма даже у одного автора изменяются различные характеристики почерка [Bhowmik et al, 2018], такие как наклон слов, строк, разгон, связность, размеры одинаковых слов и т. д.…”

Section: Introductionunclassified

HWR Text Segmentation Based on Subband Analysis

2019

NV BSU. Series Economics Computer Science

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Аннотация В работе предложен метод сегментации изображений сканированного рукописного текста на слова. Метод основан на использовании математического аппарата субполосного анализа и позволяет для заданного изображения разделить области фона и области, занимаемые текстом. Использование значений энергии фрагмента изображения, соответствующего заданной области пространственных частот, в качестве весового коэффициента позволяет точнее локализовать слова, мелкие объекты и артефакты для зашумленных изображений. Работоспособность метода подтверждают представленные результаты вычислительных экспериментов, проведенных в среде Matlab.

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Off-line Bangla handwritten word recognition: a holistic approach

Cited by 47 publications

References 46 publications

Offline hand-drawn circuit component recognition using texture and shape-based features

Offline hand-drawn circuit component recognition using texture and shape-based features

H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition

HWR Text Segmentation Based on Subband Analysis

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