A New Character Segmentation Approach for Off-Line Cursive Handwritten Words

Choudhary, Amit; Rishi, Rahul; Ahlawat, Savita

doi:10.1016/j.procs.2013.05.013

Cited by 58 publications

(19 citation statements)

References 6 publications

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“…A skew correction is included to facilitate the recognition phase in the handwritten Malayalam OCR. In 2013, Choudhary et al [15] proposed a new handwriting recognition segmentation algorithm. In this method, the segmentation points are located after the sparse image words to obtain the width of a single pixel stroke.…”

Section: Previous Research Workmentioning

confidence: 99%

Character Segmentation and Recognition for Myanmar Warning Signboard Images

Zaw¹,

Kyu²

2019

IJNDC

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A B S T R A C TThis paper publicizes the character segmentation and recognition of the Myanmar warning text signboard images taken by a mobile phone camera in natural scene. In this system, two templates are created. The first template that contains both connected pixel words and characters are used for character segmentation and the second template that contains only the connected pixel characters are used for character classification. Color enhancement process is first performed to extract the text regions. By preprocessing the color enhancement, the system can overcome the some illumination conditions. To remove the background noises on the binary images, color threshold based filtering, aspect ratio based filtering, boundary based filtering and region area based filtering techniques are used. As a next step, line segmentation and character segmentation are done. Line segmentation is performed using horizontal projection profile and character segmentation is done using vertical projection profile and bounding box methods. In the character segmentation process, template matching method is used by training connected pixel words. These connected component characters are recognized using 4 × 4 blocks based pixel density and total chain codes, four rowsbased pixel density, four columns-based pixel density and count of eight directions chain code on the whole character image and on each block of character image. This system is investigated by feature-based approach of template matching on 160 cameracaptured Myanmar warning signboards.

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Section: Previous Research Workmentioning

confidence: 99%

Character Segmentation and Recognition for Myanmar Warning Signboard Images

Zaw¹,

Kyu²

2019

IJNDC

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“…It is claimed that systems based on this type of segmentation are not useful for general text recognition. A study suggests this approach for systems in which a lexicon is statically defined, such as bank cheque recognition where vocabulary is limited [80].…”

Section: Holistic Approachmentioning

confidence: 99%

“…The principle of implicit segmentation is to utilize a sliding widow to segment the word image into frames of fixed width on which classification relies to make a decision [86]. Owing to challenges in segmentation of cursive scripts such as Arabic, researchers use the implicit segmentation approach in order to overcome the problems of word segmentation [80]. In www.ijacsa.thesai.org principle, by applying this type of segmentation, there is no need for a specific dissection algorithm for Arabic script segmentation and the accuracy performance relates to the classification performance [87].…”

Section: ) Implicit Segmentationmentioning

confidence: 99%

Printed Arabic Script Recognition: A Survey

Alghamdi¹,

Teahan²

2018

ijacsa

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Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research.

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“…[1]. OCR most often includes three procedures, text localization, character segmentation and recognition [2] [3]. The recognition is a significant zone of image processing, which is on experienced stage for machine printed content.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

2019

IJRTE

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 Abstract: Hand written text acknowledgment field has been considered as one of the hardest issues in the digital word. The multifaceted nature dimension of this exploration zone is high due to the reasons like diverse method for writing pursued by the clients, auxiliary independences, age elements of people and so on. This paper shows a novel procedure for the recognition of handwritten scripts, for example division of words and characters. In this paper, we have used two different scripts :"Devanagari" and "Roman" scripts. For which three Convolution Neural Networks(CNN) models are applied on different types of classification: one for language classification for which we have achieved 98% accuracy, second one for Devanagari character classification for which we have achieved 89% and third one for Roman character classification for which have achieved 97% respectively.

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A New Character Segmentation Approach for Off-Line Cursive Handwritten Words

Cited by 58 publications

References 6 publications

Character Segmentation and Recognition for Myanmar Warning Signboard Images

Character Segmentation and Recognition for Myanmar Warning Signboard Images

Printed Arabic Script Recognition: A Survey

Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

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