Optical character recognition (OCR) system for Roman script &amp; English language using Artificial Neural Network (ANN) classifier

Mehta, Honey; Singla, Sanjay; Mahajan, Aarti

doi:10.1109/rains.2016.7764379

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…In handwritten document generation, sometimes the negative or positive slant occur in the written words, which demands the slant correction [15]. Slant correction will help to perform character segmentation.…”

Section: Character Segmentationmentioning

confidence: 99%

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

Abbas¹,

Alhwaiti²,

Fatima³

et al. 2022

Computers, Materials &Amp; Continua

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This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today's world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.

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Section: Character Segmentationmentioning

confidence: 99%

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

Abbas¹,

Alhwaiti²,

Fatima³

et al. 2022

Computers, Materials &Amp; Continua

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“…A method for the recognition of Roman Script and English language was proposed in [21], based on Artificial Neural Network (ANN) and Nearest Neighbour (NN) to detect and interpret scanned English documents in three different font types. Using such method, the authors achieved 98% accuracy, by experimenting it on a dataset consisting of English alphabets in different fonts created by themselves, which is not available.…”

Section: Optical Character Recognitionmentioning

confidence: 99%

“…In this panorama, this article, which is an extended version of [20], focuses on the detection of hidden propaganda in mixed-code text, by proposing an algorithm for supporting the analysis of suspicious mixed-code text, based on the optical character recognition (OCR) analogy [21]. Specifically, a segregation approach is adopted to analyse the characters' combinations, which in turn are transformed into images.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

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Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

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“…An OCR allows us to store textual information in the images of documents in the form of electronic texts represented in ASCII, UNICODE, etc., requiring significantly less storage space [4]. The OCR systems can be designed for printed [11], [12], [13], [14] or handwritten scripts [15], [16], [17], [18], [19]. retrieve images based on some of their features like color, texture, shape, bag of visual words (BoVW), spatial orientation, etc.…”

Section: Literature Surveymentioning

confidence: 99%

Rough Set Based Analysis of Document Images

Chaudhuri¹

2021

Preprint

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Rough set is a well-studied subject with a theoretical foundation and many applications. However, its usage in image processing has been very sparse. Most of the well-known algorithms for document image processing related to character recognition, character spotting, and logo retrieval resort to supervised classification, causing the system to slow down in the speed with increasing diversity in the documents, as well as the need to have a large training dataset. Hence, with an aim to resolve the tediousness and pitfalls of training, but without compromising on the efficiency, we introduce a rough-set-theoretic model. It is designed to perform an unsupervised classification of optical characters and logos with a small subset of attributes, called the semi-reduct. The semi-reduct attributes are mostly geometric and topological in nature, each having a small range of discrete values estimated from different combinatorial characteristics of rough-set approximations. This eventually leads to quick and easy discernibility of almost all the characters and logos. In this thesis, we first explain the basics of rough set theory. Subsequently, we propose various attributes that can be easily computed from the binary representation of the images. In subsequent chapters we show how one can select an appropriate subset of such attributes, known as semi-reduct, to perform a document processing task. We demonstrate in this thesis that using the above attributes one can design a character recognition system that is both computationally and storage efficient. Using a different semi-reduct, we show that one can also solve the very delicate task of character spotting in ancient inscriptions. Additionally, we propose appropriate pre-processing steps to binarize the old and dilapidated inscriptions. Finally, we propose a novel technique for logo retrieval using a suitably prepared semi-reduct. Comparison with other existing techniques substantiates our claim that attributes from the rough set are indeed good candidates for document image processing.

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Optical character recognition (OCR) system for Roman script & English language using Artificial Neural Network (ANN) classifier

Cited by 18 publications

References 17 publications

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

Rough Set Based Analysis of Document Images

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