Text-based Image Segmentation Methodology

Gupta, Mehul; Ankita, Patel; Dave, Namrata; Rahul, G.; Sheth, Saurin

doi:10.1016/j.protcy.2014.08.059

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…• Line-level, word-level, and character-level segmentation: Segmentation divides the entire image into subimages to process them further. The most popular techniques used for image segmentation are: X-Y-tree decomposition [96], connected component labeling [111], Hough transforms [96], and histogram projection techniques [66], [34].…”

Section: • •mentioning

confidence: 99%

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

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Section: • •mentioning

confidence: 99%

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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“…Line segmentation followed by word segmentation give way to character level segmentation in a text image. Different level of segmentation is discussed in (Mehul et al 2014). Character level segmentation is the lowest level of segmentation which presents fundamental challenges due to variability in handwritten data.…”

Section: Need Of Segmentationmentioning

confidence: 99%

Comparative Analysis of Segmentation and Recognition Techniques for Offline Handwritten Words

Kohli¹,

Kumar²

2020

IRJASH

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A Pre-processing is the initial and vital phase in optical character recognition is the Pre-processing. Segmentation deals with the extraction of individual component from a document image. Number of techniques like projection profile, connected components, gaps between characters/components is reported in the literature for component extraction followed by feature extraction and recognition of the individual component. These techniques gives good results if components are isolated but fails if components are touched, shadowed or skewed. A novel technique is required to address such issues to enhance the recognition rate. The problem of segmentation for Roman script cursive handwriting is addressed by various authors but not enough addressed for Indian script especially Devanagari script. This paper is a review which is confined to offline handwritten script domain. It attempt to review various techniques for character segmentation considering touching characters for offline handwritten words in Devanagari script and scripts sharing similar characteristics (like Bangla, Gurumukhi), database used and their accuracy reported in the literature.

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“…Segmentation Segmentation process separates each character contained in the image. The separation process works by examining each pixel in a binary image, then a set of pixels of value 1 will separate from a binary set of values 1 in the same image to facilitate the next process [9]. The segmented character is then selected for narrowing of the character thickening to facilitate analysis in the feature extraction process without leaving characteristics of the characters, otherwise, it can reduce the size of the image data which can speed up further processing [10].…”

Section: A Preprocessingmentioning

confidence: 99%

Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format

Anugrah

Bintoro

2017

indones.j.electron.telecommun.

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Printed media is still popular in nowadays society. Unfortunately, such media encountered several drawbacks. For example, this type of media consumes large storage that impact in high maintenance cost. To keep printed information more efficient and long-lasting, people usually convert it into digital format. In this paper, we built Optical Character Recognition (OCR) system to enable automatic conversion the image containing the sentence in Latin characters into digital text-shaped information. This system consists of several interrelated stages including preprocessing, segmentation, feature extraction, classifier, model and recognition. In preprocessing, the median filter is used to clarify the image from noise and the Otsu's function is used to binarize the image. It followed by character segmentation using connected component labeling. Artificial neural network (ANN) is used for feature extraction to recognize the character. The result shows that this system enable to recognize the characters in the image whose success rate is influenced by the training of the system.

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Text-based Image Segmentation Methodology

Cited by 19 publications

References 10 publications

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Comparative Analysis of Segmentation and Recognition Techniques for Offline Handwritten Words

Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format

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