Bangla Optical Character Recognition and Text-to-Speech Conversion using Raspberry Pi

Rajbongshi, Aditya; Ibadul, Md.; Amin, Al; Mahbubur, Md.; Majumder, Anup; Ezharul, Md.

doi:10.14569/ijacsa.2020.0110636

Cited by 11 publications

(9 citation statements)

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“…With this tool, the text of an image is extracted and afterward is synthesized to speech generally using Google Text-to-Speech Engine (GTTS) [116]. In this set of articles, the authors of references [174], [175] describe some enhancements to their developments such as appending a higher resolution camera to increase the accuracy of text extraction, reduce the noise of the speech using signal processing techniques, and implementing a method to identify mathematical equations through the application of parallel core processing techniques in the Raspberry Pi. Specifically, study [174] was tested by a blind user.…”

Section: Educational Devices and Materialsmentioning

confidence: 99%

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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Disabled People deal with a series of barriers that limit their inclusion, empowerment, wellbeing, and role in society with a special emphasis in low and medium-income countries. One of these barriers is concerning the accessibility and affordability of assistive technologies (ATs) that help to enhance the quality of life of these persons. In this context, this systematic literature review (SLR) analyzes and describes how free and open-source hardware (OSHW) and open software (OSS) are employed in the design, development, and deployment of low-cost ATs. In the SLR process, different ATs were analyzed for disabilities such as visual, mobility, upper body, prostheses, hearing & speaking, daily living, and participation in society. The ATs were designed with diverse OSHW and OSS technologies such as Arduino, Raspberry Pi, NVidia Jeston, OpenCV, YOLO, MobileNet, EEG and EMG signal conditioning devices, actuators, and sensors such as ultrasonic, LiDar, or flex. 809 studies were collected and analyzed from the database Web of Science, GitHub, and the specialized journals in OSHW HardwareX and the Journal of Open Hardware during the years 2013-2022. In the first part of the SLR, the bibliometric trends and topic clusters regarding the selected studies are described. Secondly, the ATs identified with open source technologies, e.g., sensor-based or computer vision-based, are described along with a complete state-ofart about these based on each disability recognized. Finally, the issues and challenges to this approach are explored including technical factors, documentation, government policies, and the inclusion of disabled people in open source co-creation. The purpose of this study is to inform practitioners, designers, or stakeholders about low-cost (frugal) ATs with OSHW and OSS, and thus promote their development, accessibility, and affordability, contributing to benefit the community of disabled people. INDEX TERMS Assistive technologies, disabled people, persons with disabilities, adaptive aids, open hardware, open-source hardware, open-source software, systematic literature review.

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Section: Educational Devices and Materialsmentioning

confidence: 99%

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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“…Although this work collected datasets of 5880 of these characters and trained them to be trained by Tesseract OCR methods, the paper only presented a classification of such types of characters. In the same context, the study [14] addressed the issue of understanding images including Bangla text. However, the limitation of this work is that it only involves extracting Bangla texts from corresponding images.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

Jwaid

2022

EEJET

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Text recognition of images is beneficial in a wide range of computer vision purposes such as robot navigation, document analysis, and image search. The optical character recognition (OCR) technique presents a simple tool to combine text recognition functionality to many industrial and educational applications. Best OCR results can be acquired when the background of the text image is uniform and appears as a document picture. In contrast, the challenges to recognizing accurate texts occur when the image has a non-uniform background that require further preprocessing to obtain acceptable OCR result. This work discusses three scenarios. Initially, this work will test the OCR on a normal business card as an image with a uniform background. Next, discusses the text recognition of a keypad image including digits with a non-uniform background. Here, there are two preprocessing algorithms used to enhance the OCR function to overcome the negative effect of the non-uniform background of images and to detect text with high accuracy. Finally, the developed OCR method is tested on different scanned bills and discusses the variation of the obtained results. The two algorithms are the morphological reconstruction to eliminate artifacts and create cleaner images to be further processed by OCR and the Region of Interest ROI-based OCR to spot explicit regions in a tested image. Verification for the effectiveness of the Morphological-based OCR over the ROI-based method has been conducted on a dataset of scanned electricity bills images with an accuracy of 98.2 % for Morphological-based while it is only about 89.3 % for ROI-based OCR.

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“…One doesn't need a deep knowledge of it to use. Rajbongshi et al (2020) reiterated in their study that, raspberry pi is a Linux-based system, which makes it easier for project development. They mentioned that most of the libraries for text-to-speech conversions are compatible and easily installed on Linux systems.…”

Section: The Advantages and Disadvantages Of Using Raspberry Pi Systems For Developersmentioning

confidence: 99%

“…Despite the researches carried out in this area of study, there still some setbacks in the development of these systems. Rajbongshi et al (2020) observed that there was no system developed to read the handwritten text, this they predicted should be an area future researches should be undertaken. [31] Dunai et al (2017), for detection fake banknote for the visually impaired, there is a missing gap in arear of research and they proposed, this should be researched further in the future to assist visually impaired to be able to identify and differentiate fake banknotes as well as other documents.…”

Section: Limitations Of the Current Research On Raspberry Pi Projectsmentioning

confidence: 99%

The Review of Raspberry Pi Based - Systems to Assist the Disabled Persons

2021

IJATCSE

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As people, eyesight is one of the most significant and depended upon in acquainting ourselves with where we are and what is around us. Freedom in exploring our new environment has to do with our capacity to survey these new environments, distinguish impediments as well as distinguish between objects in these environments. This task as simple as it may seem is one of the many problems encountered by the blind in their daily lives. Which hinders their ability to freely move about and carry out their day-to-day activities. Many types of research have been carried out to develop systems to assist the blind and the deaf. Some of these researches were developed with microcomputers as their central component. This study seeks to investigate the literature for studies that utilized raspberry pi to develop systems to assist the blind and the deaf. It as well attempts to define future research perspectives on the subject by addressing the research questions stated in this study. The finding of this study would enlighten researchers to be guided for future researches, as well as enable them to see the flaws in the area of study in other to solve such problems.

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Bangla Optical Character Recognition and Text-to-Speech Conversion using Raspberry Pi

Cited by 11 publications

References 0 publications

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

The Review of Raspberry Pi Based - Systems to Assist the Disabled Persons

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