Text extraction from images using gamma correction method and different text extraction methods &amp;#x2014; A comparative analysis

Devi, G. Gayathri; Sumathi, C. P.

doi:10.1109/icices.2014.7033973

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…The GCM [7] is an important method to extract text from a complex color image by removing the background and extracting the text. Indeed, the GCM method compute from the input image one hundred modified images by variation the γ value from 0.1 to 10.0 using 0.1 as increment step.…”

Section: Gcm Overviewmentioning

confidence: 99%

“…The most important method developed for text extraction is the Gamma Correction Method (GCM) [6]. In fact, the GCM method outperforms the existing methods by suppress completely the background while conserving the features of the text [7]. In this method the Gray-Level Co-occurrence Matrix (GLCM) is calculated to determine the energy and the contrast.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient HW/SW Design for Text Extraction from Complex Color Image

Atitallah¹,

Kachouri²,

Atitallah³

et al. 2022

Computers, Materials &Amp; Continua

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In the context of constructing an embedded system to help visually impaired people to interpret text, in this paper, an efficient High-level synthesis (HLS) Hardware/Software (HW/SW) design for text extraction using the Gamma Correction Method (GCM) is proposed. Indeed, the GCM is a common method used to extract text from a complex color image and video. The purpose of this work is to study the complexity of the GCM method on Xilinx ZCU102 FPGA board and to propose a HW implementation as Intellectual Property (IP) block of the critical blocks in this method using HLS flow with taking account the quality of the text extraction. This IP is integrated and connected to the ARM Cortex-A53 as coprocessor in HW/SW codesign context. The experimental results show that the HLS HW/SW implementation of the GCM method on ZCU102 FPGA board allows a reduction in processing time by about 89% compared to the SW implementation. This result is given for the same potency and strength of SW implementation for the text extraction.

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Section: Gcm Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient HW/SW Design for Text Extraction from Complex Color Image

Atitallah¹,

Kachouri²,

Atitallah³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…More generally, it is local variants applied inside the boxes containing localized texts that are implemented to take into account the problem of complex backgrounds. Sumathi et al [20,21] proposed an efficient method to extract text from complex color images based on the gamma correction method (GCM). This method allows us to remove the background in order to extract the text from the image.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

Atitallah¹,

Atitallah²,

Said³

et al. 2023

Computer Systems Science and Engineering

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The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma correction method (GCM); and an optical character recognition (OCR) algorithm for text recognition. A detailed complexity study of the different blocks of this text recognition system has been realized. Following this study, an acceleration of the GCM algorithm (AGCM) is proposed. The AGCM algorithm has reduced the complexity in the text recognition system by 70% and kept the same quality of text recognition as that of the original method. To assist visually impaired persons, a graphical interface of the entire text recognition chain has been developed, allowing the capture of images from a camera, rapid and intuitive visualization of the recognized text from this image, and text-to-speech synthesis. Our text recognition system provides an improvement of 6.8% for the recognition rate and 7.6% for the F-measure relative to GCM and AGCM algorithms.

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