A Three-Phase Noise Removal Approach to Achieve Accuracy in Line Segmentation of Odia Text

Kar, Aradhana; Pradhan, Sateesh Kumar

doi:10.1109/ocit53463.2021.00022

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…2. The white spaces at the margins that enclose the text in image 'B' are removed using the RemoveBoundarySpaces phase of the RemoveNoise module of (Kar and Pradhan, 2021), and the result is stored in 'C'. 3.…”

Section: Algorithmmentioning

confidence: 99%

“…The work of the Group_Vacant_Quads is to iterate through the quadrants of an alphabet and group it based on the first vacant quadrant found. The Segment_Line part segments line texts from the document image using the Line Segmenting module of the system described in the research paper (Kar and Pradhan, 2021). The Character_Segment part has been explained in this study and this part segments each character from the segmented line texts.…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Recognise Odia Characters by Extracting Features from Fragmented Quadrants

Kar¹,

Pradhan²

2023

Journal of Computer Science

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The system introduced in this study concentrates on the recognition of Odia characters. The input to the system is an image in .jpg format which has been created by scanning a document or converting a document to an image by software. The document consists of all Odia alphabets written using the font family 'AkrutiOriAshok-99' in a particular font size in bold style. The font sizes that have been considered are 18, 20, 22, 24, 26, 28, 36, 48, and 72. The noise is removed and line texts are segmented from the input image. Then individual alphabets are segmented from a line text using the Character_Segment part of the system proposed in this study. Then the features are extracted from the segmented individual alphabet and the features are searched in the dictionary of features stored in an excel file for a correct match one by one. The proposed system is also responsible for creating the dictionary of features of all Odia alphabets. Hence, the system in this study has four parts, Segment_Line, Character_Segment, Build_Dictionary, and Recognise_Character. According to the testing results, the system proposed in this study has achieved a correctness accuracy of recognition of Odia alphabets of 99.8%.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Approach to Recognise Odia Characters by Extracting Features from Fragmented Quadrants

Kar¹,

Pradhan²

2023

Journal of Computer Science

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“…2. The white spaces that surround the text in the gray image are removed using Phase -I of RemoveNoise module of [3], that is, RemoveBoundarySpaces. This gives an image that consists of Odia alphabet only.…”

Section: Algorithmmentioning

confidence: 99%

“…The input image is converted to gray image. The white spaces surrounding the Odia alphabet in the gray image are removed using the Phase -1 of RemoveNoise module of [3] (RemoveBoundarySpaces). Then the resultant image is resized into the dimension p x q (p = 64 and q = 64) where, 'p' is the number of rows and 'q' is the number of columns.…”

Section: A) Preprocessing Modulementioning

confidence: 99%

“…If in some cases, these two modules of Recognition are unable to find a correct match, the TraceAnotherDirection module of Recognition has been used. The Preprocessing module in 'DictionaryBuilding' and 'FindingMatch' converts the image into gray image and then the white spaces surrounding the Odia alphabet in the gray image are removed using the Phase -1 of RemoveNoise module of [3] (RemoveBoundarySpaces). For converting image to gray, OpenCv package of python has been used.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Odia Character in an Image by Dividing the Image into Four Quadrants

Kar¹,

Pradhan²

2022

IJACSA

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This paper deals with optical character recognition of Odia characters written in a particular font family 'AkrutiOriAshok-99' with different font sizes 18, 20, 22, 24, 26, 28, 36, 48 and 72 in Bold style. The font 'AkrutiOriAshok-99' is a font from the typing software 'Akruti'. The basic idea behind the approach followed in this paper is the character decomposition into four quadrants and then extracting features from each quadrant. The image processing techniques like converting the image to gray, resizing of image and converting gray image to binary are used in this approach. The system explained in this paper has two major parts: DictionaryBuilding and FindingMatch. For DictionaryBuilding, dictionary of images which are created either by scanning a document or a document converted to image, both written in same font family in different sizes. The features are extracted from each image in any font size in the 'Dictionary' using Preprocessing, FindPath, GettingFeaturesLeft or GettingFeaturesRight, VisitSubQuad, RemainingSubQuad, WriteToExcel and CommonFeature modules. The part FindingMatch is responsible for finding a correct match in the dictionary for the input image. For this, FeatureExtraction and Recognition modules have been used. Longest Common Subsequence (LCS) has been used for finding the common feature in DictionaryBuilding as well as finding the correct match. A total of 1800 characters, 200 characters of each font size have been tested and 98.1% of correctness has been achieved.

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Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion

Kar

Pradhan

2022

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)

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A Three-Phase Noise Removal Approach to Achieve Accuracy in Line Segmentation of Odia Text

Cited by 5 publications

References 3 publications

An Approach to Recognise Odia Characters by Extracting Features from Fragmented Quadrants

An Approach to Recognise Odia Characters by Extracting Features from Fragmented Quadrants

Recognition of Odia Character in an Image by Dividing the Image into Four Quadrants

Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion

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