K-Algorithm: A Modified Technique for Noise Removal in Handwritten Documents

Bansal, Kanika; Kumar, Rajiv

doi:10.5121/ijist.2013.3301

“…Across the OCR models we considered, Noise-Removal (NR) techniques also varied significantly. In one framework, the K-Algorithm NR technique [4] operated a modified Median blurring algorithm, M ((i − x, j − x) . .…”

Section: Noise Removalmentioning

confidence: 99%

Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits

Sambath

2023

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The automated transcription of handwritten characters into a legible output is a multi-faceted process with diverse applications. In this paper, a novel approach to optical character recognition (OCR) for handwritten digits is proposed that, in certain components, exceeds current architectures in terms of accuracy, effectiveness, adjustability, temporal efficiency, and/or computational simplicity. This model succeeds in the adoption and enhancement of deprecated or obsolete algorithms across eight steps of image pre-processing—normalization, grayscaling, thresholding/binarization, noise removal, skew-correction, skeletonization/thinning, line separation, and character segmentation. The aforementioned model is evaluated with the use of a Convolutional Neural Network (CNN) leveraging the Balanced eMNIST dataset for training and testing. By suggesting contour-based feature extraction methods as alternatives to pixel-by-pixel iteration, the proposed paradigm demonstrates its capacity to serve as a suitable alternative to current, commonly used algorithms and computational techniques for textual image classification.

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“…Across the OCR models we considered, Noise-Removal (NR) techniques also varied significantly. In one framework, the K-Algorithm NR technique [11] operated a modified Median blurring algorithm, M ((i − x, j − x) . .…”

Section: Noise Removalmentioning

confidence: 99%

Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits

Sambath

2023

Preprint

0

View full text Add to dashboard Cite

The automated transcription of handwritten characters into a legible output is a multi-faceted process with diverse applications. In this paper, a novel approach to optical character recognition (OCR) for handwritten digits is proposed that, in certain components, exceeds current architectures in terms of accuracy, effectiveness, adjustability, temporal efficiency, and/or computational simplicity. This model succeeds in the adoption and enhancement of deprecated or obsolete algorithms across eight steps of image pre-processing—normalization, grayscaling, thresholding/binarization, noise removal, skew-correction, skeletonization/thinning, line separation, and character segmentation. The aforementioned model is evaluated with the use of a Convolutional Neural Network (CNN) leveraging the Balanced eMNIST dataset for training and testing. By suggesting contour-based feature extraction methods as alternatives to pixel-by-pixel iteration, the proposed paradigm demonstrates its capacity to serve as a suitable alternative to current, commonly used algorithms and computational techniques for textual image classification.

show abstract

“…transmission channel distortions, room reverberation, microphone filtering) (Xiong, 2009). In Optical Character Recognition, noise refers to "the error in the pixel value or an unwanted bit pattern, which do not have any significance in the output", where the unwanted bit patterns are introduced by uneven writing surface or poor quality of the data acquisition device and include textured or coloured background (Bansal and Kumar, 2013). Different handwriting however is not seen as noise which needs removing but as a problem which needs to be addressed.…”

Section: Related Workmentioning

confidence: 99%