Trending Bestseller

Segmentation-Verification for Handwritten Digit Recognition

Abdeljalil Gattal

No reviews yet Write a Review
Paperback / softback
11 September 2017
$131.00
Ships in 3-5 business days
Hurry up! Current stock:
Doctoral Thesis / Dissertation from the year 2016 in the subject Computer Science - Applied, National Higher School Of Computer Engineering, language: English, abstract: Automatic reading of digit fields from an image document has been proposed in several applications such as bank checks, postal code and forms. In this context, two main problems occur when attempting to design a handwritten digit string recognition system. The first problem is the link between adjacent digits, which can be naturally spaced, overlapped or/and connected. The second problem is the unknown length of the digit string, which is not carefully written by people in real-life situations.In this thesis, SVM-based segmentation-verification system for segmenting two connected handwritten digits using the oriented sliding window is proposed. It employs a segmentation-verification system using conjointly the oriented sliding window and Support Vector Machine (SVM) classifiers. Experimental results showed that the proposed system is more appropriate for segmenting simple and multiple connections. Its main advantage lays in the use few rules for finding the optimal segmentation path. Hence, the proposed approach constitutes a tradeoff between the correct segmentation and the number of the segmentation cuts. Thereafter, we propose a new design of a handwritten digit string recognition system based on the explicit approach for the unknown-length digit strings. Three methods are combined according the link of adjacent digits, which are the histogram of the vertical projection dedicated for spaced digits, the contour analysis dedicated for overlapped digits and the Radon transform performed on the sliding window dedicated for connected digits. A recognition and verification module based on Support Vector Machine (SVM) classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Experimental results conducted on the benchmark dataset show that the proposed system

This product hasn't received any reviews yet. Be the first to review this product!

$131.00
Ships in 3-5 business days
Hurry up! Current stock:

Segmentation-Verification for Handwritten Digit Recognition

$131.00

Description

Doctoral Thesis / Dissertation from the year 2016 in the subject Computer Science - Applied, National Higher School Of Computer Engineering, language: English, abstract: Automatic reading of digit fields from an image document has been proposed in several applications such as bank checks, postal code and forms. In this context, two main problems occur when attempting to design a handwritten digit string recognition system. The first problem is the link between adjacent digits, which can be naturally spaced, overlapped or/and connected. The second problem is the unknown length of the digit string, which is not carefully written by people in real-life situations.In this thesis, SVM-based segmentation-verification system for segmenting two connected handwritten digits using the oriented sliding window is proposed. It employs a segmentation-verification system using conjointly the oriented sliding window and Support Vector Machine (SVM) classifiers. Experimental results showed that the proposed system is more appropriate for segmenting simple and multiple connections. Its main advantage lays in the use few rules for finding the optimal segmentation path. Hence, the proposed approach constitutes a tradeoff between the correct segmentation and the number of the segmentation cuts. Thereafter, we propose a new design of a handwritten digit string recognition system based on the explicit approach for the unknown-length digit strings. Three methods are combined according the link of adjacent digits, which are the histogram of the vertical projection dedicated for spaced digits, the contour analysis dedicated for overlapped digits and the Radon transform performed on the sliding window dedicated for connected digits. A recognition and verification module based on Support Vector Machine (SVM) classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Experimental results conducted on the benchmark dataset show that the proposed system

Customers Also Viewed

Buy Books Online at BookLoop

Discover your next great read at BookLoop, Australiand online bookstore offering a vast selection of titles across various genres and interests. Whether you're curious about what's trending or searching for graphic novels that captivate, thrilling crime and mystery fiction, or exhilarating action and adventure stories, our curated collections have something for every reader. Delve into imaginative fantasy worlds or explore the realms of science fiction that challenge the boundaries of reality. Fans of contemporary narratives will find compelling stories in our contemporary fiction section. Embark on epic journeys with our fantasy and science fiction titles,

Shop Trending Books and New Releases

Explore our new releases for the most recent additions in romance books, fantasy books, graphic novels, crime and mystery books, science fiction books as well as biographies, cookbooks, self help books, tarot cards, fortunetelling and much more. With titles covering current trends, booktok and bookstagram recommendations, and emerging authors, BookLoop remains your go-to local australian bookstore for buying books online across all book genres.

Shop Best Books By Collection

Stay updated with the literary world by browsing our trending books, featuring the latest bestsellers and critically acclaimed works. Explore titles from popular brands like Minecraft, Pokemon, Star Wars, Bluey, Lonely Planet, ABIA award winners, Peppa Pig, and our specialised collection of ADHD books. At BookLoop, we are committed to providing a diverse and enriching reading experience for all.