ATMA: Android Travel Mate Application

Mishra, Nitin; Patvardhan, C.

doi:10.5120/7852-1083

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…ATMA: android travel mate application [30] proposed by Mishra, Nitin, and C. Patvardhan, that It empowers Tourists and Travellers to effortlessly catch the native signboards, nation dialect Books pages, hotel menus, banners and so on. Unicode text format was obtained from content embedded in the caught image by an implicit OCR.…”

Section: Atma: Android Travel Mate Applicationmentioning

confidence: 99%

A Detailed Analysis of Optical Character Recognition Technology

Hamad

Kaya

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:In many different fields, there is a high demand for storing information to a computer storage disk from the data available in printed or handwritten documents or images to later re-utilize this information by means of computers. One simple way to store information to a computer system from these printed documents could be first to scan the documents and then store them as image files. But to re-utilize this information, it would very difficult to read or query text or other information from these image files. Therefore a technique to automatically retrieve and store information, in particular text, from image files is needed. Optical character recognition is an active research area that attempts to develop a computer system with the ability to extract and process text from images automatically. The objective of OCR is to achieve modification or conversion of any form of text or text-containing documents such as handwritten text, printed or scanned text images, into an editable digital format for deeper and further processing. Therefore, OCR enables a machine to automatically recognize text in such documents. Some major challenges need to be recognized and handled in order to achieve a successful automation. The font characteristics of the characters in paper documents and quality of images are only some of the recent challenges. Due to these challenges, characters sometimes may not be recognized correctly by computer system. In this paper we investigate OCR in four different ways. First we give a detailed overview of the challenges that might emerge in OCR stages. Second, we review the general phases of an OCR system such as pre-processing, segmentation, normalization, feature extraction, classification and post-processing. Then, we highlight developments and main applications and uses of OCR and finally, a brief OCR history are discussed. Therefore, this discussion provides a very comprehensive review of the state-of-the-art of the field.

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Section: Atma: Android Travel Mate Applicationmentioning

confidence: 99%

A Detailed Analysis of Optical Character Recognition Technology

Hamad

Kaya

2016

International Journal of Applied Mathematics, Electronics and Computers

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“…It likewise gives interpretation office with the goal that Tourists can translate Native Language Unicode content into their own particular nation language. This Application has advanced features such as copy paste, share and search for travel related inquiries like museums, spots, hotels, books, restaurants and so forth [7].…”

Section: Related Workmentioning

confidence: 99%

Signboard Text Translator: A Guide to Tourist

Kavati

Kumar

Kesagani

et al. 2017

IJECE

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The travelers face troubles in understanding the signboards which are written in local language. The travelers can rely on smart phone for traveling purposes. Smart phones become most popular in recent years in terms of market value and the number of useful applications to the users. This work intends to build up a web application that can recognize the English content present on signboard pictures captured using a smart phone, translate the content from English to Telugu, and display the translated Telugu text back onto the screen of the phone. Experiments have been conducted on various signboard pictures and the outcomes demonstrate the viability of the proposed approach.

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“…Original paper [30] has average runtime of 681 ms and the experiment results show the runtime of 459 ms keeping the accuracy almost the same. The consistency of system for English OCR is also the same.…”

Section: Resultsmentioning

confidence: 88%

“…This is yet another OCR engine targeting mobile devices. Most of the features are same as Tesseract and GOCR but the one main feature that stands it apart is that it has very small Some other papers [28], [29] and [30] that provide good insight on Hindi OCR are as the initial steps in the systems architecture. These research works for Hindi OCR apply to both desktop and mobile applications.…”

Section: Javaocrmentioning

confidence: 99%

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Improving the Efficiency of Tesseract Ocr Engine

Badla¹

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ATMA: Android Travel Mate Application

Cited by 6 publications

References 4 publications

A Detailed Analysis of Optical Character Recognition Technology

A Detailed Analysis of Optical Character Recognition Technology

Signboard Text Translator: A Guide to Tourist

Improving the Efficiency of Tesseract Ocr Engine

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