scholarly journals Optical Character Reader & Text To Speech Conversion using Correlations & Speech Synthesis

2020 ◽  
Vol 9 (10) ◽  
pp. 478-483
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Figurative Language ◽  
Text To Speech ◽  
Rate System ◽  
Written Text ◽  
Optical Character ◽  
Effective Manner ◽  
High Level

In the modern era of image processing, recognizing content or information from an image is process of electronic conversion into machine encoded text. Advanced systems that are capable of producing high accuracy for multi-font recognition are now becoming commonplace, and with the support of digital consent formatting. Some programs are able to retrieve formats that are very close to the original page including images, columns, and other non-text items. Proposed system is able to recognize text from an image and convert it into editable text along with speech conversion. System uses Correlation model for OCR (Optical Character Recognition) and Speech Synthesis for TTS (Text To Speech) conversion. Correlation is a measurement of the similarities between two similar objects such as the predefined alphabets and recognizing a combination of those alphabets from an image. Speech synthesis is an artificial expression of human speech. The computer program that has been used this feature is called a speech computer as well as speech synthesizer that can be implemented on the basis of software or hardware primitives. The text-to-speech system (TTS) converts a standard language text into a speech; some programs provide figurative language presentations such as typed text in speech. System is capable enough to acquire high level of accuracy with less false recognition. It is required to built an effective text scanner that can recognize text from an image with less error rate. System has been implemented in MATLAB and various pre-processing filters have been applied for better enhancement and extraction. Hand written text can also be recognized with an effective manner.

Smart Reader for Visually Challenged Using Optical Character Recognition and Text-To-Speech

2020 ◽  
pp. 205-208
Author(s):  
Sowmya R ◽  
Sushma S Jagtap ◽  
Gnanamoorthy Kasthuri
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Read Aloud ◽  
Text To Speech ◽  
Digital Text ◽  
Optical Character ◽  
The People ◽  
Text To Speech Synthesis ◽  
Visually Challenged

Assistive technology uses assistive, adaptive and rehabilitative devices for people with disabilities. It’s assessed there are about 36 million people with visual impairment in the world and a further 216 million who lead life with moderate to severe visual impairments. Leveraging technology has helped the visually challenged in carrying out tasks on par with the people blessed with vision particularly in the activities of reading and writing. In the proposed work, an image scanning device attached to a microcontroller is designed. This device is designed in the form of hand gloves for ease of usage. The glove with the camera at the fingertip, when rolled over lines of text, scans the information and converts it into digital text with Optical Character Recognition (OCR). The converted digital text is finally read aloud using Text-to-speech synthesis. The results obtained were accurate and met the standards of operability.

Kurzweil Reading Machine: A Partial Evaluation of Its Optical Character Recognition Error Rate

1979 ◽  
Vol 73 (10) ◽  
pp. 389-399
Author(s):  
Gregory L. Goodrich ◽  
Richard R. Bennett ◽  
William R. De L'aune ◽  
Harvey Lauer ◽  
Leonard Mowinski
Keyword(s):  
Error Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Partial Evaluation ◽  
Error Rates ◽  
Recognition Error ◽  
Optical Character ◽  
Printed Materials ◽  
High Level

This study was designed to assess the Kurzweil Reading Machine's ability to read three different type styles produced by five different means. The results indicate that the Kurzweil Reading Machines tested have different error rates depending upon the means of producing the copy and upon the type style used; there was a significant interaction between copy method and type style. The interaction indicates that some type styles are better read when the copy is made by one means rather than another. Error rates varied between less than one percent and more than twenty percent. In general, the user will find that high quality printed materials will be read with a relatively high level of accuracy, but as the quality of the material decreases, the number of errors made by the machine also increases. As this error rate increases, the user will find it increasingly difficult to understand the spoken output.

Penerapan Histogram Equalization pada Optical Character Recognition Preprocessing

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Peter Pangestu
Keyword(s):  
Contrast Enhancement ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Histogram Equalization ◽  
Optical Character ◽  
High Level ◽  
Enhancement Algorithm

In this paper, we will discuss about the implementatoion of Histogram Equalization for images contrast enhancement, in the preprocessing step, on Optical Character Recognition(OCR). OCR has several steps, including preprocessing step. Implementing images contrast enhancement algorithm will make it easier.  It is important for images to have high level contrast. It makes those images clear. Changing the histogram will make the colors of images also change. The output will be taken to the next step processing and we will get more accurate recognition.

scholarly journals Aplikasi Kalkulator Tulisan Tangan Sederhana Menggunakan Optical Character Recognition (OCR)

2021 ◽  
Vol 3 (2) ◽  
pp. 103-116
Author(s):  
Supriadi Supriadi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Text Recognition ◽  
Arithmetic Operations ◽  
Written Text ◽  
Optical Character ◽  
Calculation Results

The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform arithmetic operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user will see the written text and remember it again so that it takes longer to perform calculations using a calculator. The method used in this study is Optical Character Recognition, this method can recognize text contained in images or handwritten images of mathematical number operations. The results of the text recognition will then be carried out by arithmetic calculations to get the calculation results. From the trials on 20 handwritten images of mathematical number operations, the results obtained were 85% accuracy of extraction and accuracy of handwritten images that can be calculated and correct by 85%

scholarly journals Simple Handwriting Calculator Application Using Optical Character Recognition (OCR)

2021 ◽  
Vol 2 (3) ◽  
pp. 163
Author(s):  
Supriadi Supriadi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Written Text ◽  
Optical Character ◽  
Calculation Results

The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform calculation operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user will see the written text and remember it again so that it takes longer to perform calculations using a calculator. The method used in this study is Optical Character Recognition, this method can recognize text contained in images or handwritten images of mathematical number operations. The results of the introduction of the text will then be carried out by arithmetic calculations to get the calculation results. From the trials on 20 handwritten images of mathematical number operations, the results obtained were 85% accuracy of extraction and accuracy of handwritten images that can be calculated and correct by 85%.

scholarly journals Expiry date recognition using deep neural networks

2020 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Traian Rebedea ◽  
Vlad Florea
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Efficient Solution ◽  
Deep Neural Networks ◽  
Accuracy Improvement ◽  
Expiry Date ◽  
Food Items ◽  
Optical Character

This paper proposes a deep learning solution for optical character recognition, specifically tuned to detect expiration dates that are printed on the packaging of food items. This method can be used to reduce food waste, having a significant impact on the design of smart refrigerators and can prove especially useful for persons with vision difficulties, by combining it with a speech synthesis engine. The main problem in designing an efficient solution for expiry date recognition is the lack of a large enough dataset to train deep neural networks. To tackle this issue, we propose to use an additional dataset composed of synthetically generated images. Both the synthetic and real image datasets are detailed in the paper and we show that the proposed method offers a 9.4% accuracy improvement over using real images alone.

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