Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform

Vaidya, Madhav; Joshi, Yashwant; Bhalerao, Milind

doi:10.22266/ijies2017.1231.09

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…Preprocessing method OTSU is used to make the image into binary image [20]. The resultant image will be a bianary image without noise that is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Identification of Broken Characters in Degraded Documents

Ramalingam¹,

Bhojan²

2018

IJIES

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Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing.

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“…Preprocessing method OTSU is used to make the image into binary image [20]. The resultant image will be a bianary image without noise that is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Identification of Broken Characters in Degraded Documents

Ramalingam¹,

Bhojan²

2018

IJIES

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“…The lossy compression technique is used in certain image processing applications involving large image sizes, high resolutions such as satellite images and video processing to achieve a good compression ratio (Vaidya, 2017). Lossless image compression is used in other applications where every data component is important, such as medical imaging and document images.…”

Section: Sequential Coding Schemesmentioning

confidence: 99%

Untitled

2022

3C Tecnología

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Section: Sequential Coding Schemesmentioning

confidence: 99%

Verification of role of data scanning direction in image compression using fuzzy composition operations

Paikrao¹,

Doye²,

Bhalerao³

et al. 2022

3C Tecnología

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A digital image is a numerical representation of visual perception that can be manipulated according to specifications. In order to reduce the cost of storage and transmission, digital images are compressed. Depending upon the quality of reconstruction, compression methods are categorized as Lossy and Lossless compression. The lossless image compression techniques, where the exact recovery of data is possible, is the most challenging task considering the tradeoff between the compression ratio achieved and the quality of reconstruction. The inherent data redundancies like interpixel redundancy and coding redundancy in the image are exploited for this purpose. The interpixel redundancy is treated by decorrelation using Run-length Encoding, Predictive Coding, and other Transformation Coding techniques. While entropy-based coding can be achieved using Huffman codes, arithmetic codes, and the LZW algorithm, which eliminates the coding redundancy. During the implementation of these sequential coding algorithms, the direction used for data scanning plays an important role. A study of various image compression techniques using sequential coding schemes is presented in this paper. The experimentation on 100 gray-level images comprising 10 different classes is carried out to understand the effect of the direction of scanning of data on its compressibility. Depending upon this study the interrelation between the maximum length of the Run and compression achieved similarly the resultant number of Tuples and compression achieved is reported. Considering the fuzzy nature of these relations, fuzzy composition operations like max-min, min-max, and max-mean compositions are used for decision-making. In this way, a rational comment on which data scanning direction is suitable for a particular class of images is made in the conclusion.

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Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform

Cited by 4 publications

References 31 publications

Identification of Broken Characters in Degraded Documents

Identification of Broken Characters in Degraded Documents

Untitled

Verification of role of data scanning direction in image compression using fuzzy composition operations

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