Improving Various Offline Techniques used for Handwritten Character Recognition : A Review

Nath, Rajiv Kumar; Rastogi, Mayuri

doi:10.5120/7726-1136

“…This feature is developed because the author facing limitation in finding digital handwritten text dataset collection. In the previous study, the researchers used scanned of handwriting image as their dataset [3][12][14] [15]. With total of 50 sets of image format digital handwritten image including the total of 323 characters as the dataset of the research.…”

Section: Dataset Preparationmentioning

confidence: 99%

The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract

Mursari

¹

,

Wibowo

²

2021

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Optical Character Recognition (OCR) has been widely discussed in various topics in the rise of robotics, artificial intelligence and computer vision. OCR has become a solution in extracting characters from the image into machine-encoded text. This research aims to discuss character recognition from digital handwritten image. However, characters recognition problems using OCR has been more or less solved. OCR mainly implemented in reading characters from scanned of printed documents. In this research, image preprocessing including convert to grayscale, morphological operations and noise removal has been successfully boost the accuracy score of OCR performance. The average success outcome resulted to 79.26% in reading characters from the image.

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“…The approaches were selected depending on the types of alphabets and characters read [19]. Nath and Rastogi [20] developed some stages in optical character recognition (OCR). The segmentation was done using the explicit and implicit approaches.…”

Section: Introductionmentioning

confidence: 99%

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Munsarif,

Saman

2024

Comput. Sci. Inf. Technol.

0

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One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%.

show abstract

“…The approaches were selected depending on the types of alphabets and characters read [19]. Nath and Rastogi [20] developed some stages in optical character recognition (OCR). The segmentation was done using the explicit and implicit approaches.…”

Section: Introductionmentioning

confidence: 99%

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Munsarif,

Saman

2024

Comput Sci Inf Technol

0

View full text Add to dashboard Cite

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%.

show abstract

Improving Various Offline Techniques used for Handwritten Character Recognition : A Review

Cited by 5 publications

References 25 publications

The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract

The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters

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