scispace - formally typeset
Search or ask a question
Topic

Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.


Papers
More filters
Proceedings ArticleDOI
01 Nov 2008
TL;DR: Zone and Distance metric based feature extraction system is presented and 98 % and 96 % recognition rate for Kannada and Telugu numerals respectively are obtained.
Abstract: Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we present Zone and Distance metric based feature extraction system. The character centroid is computed and the image is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. This procedure is repeated for all the zones present in the numeral image. Finally n such features are extracted for classification and recognition. Feed forward back propagation neural network is designed for subsequent classification and recognition purpose. We obtained 98 % and 96 % recognition rate for Kannada and Telugu numerals respectively.

37 citations

Journal ArticleDOI
TL;DR: This work forms the word segmentation problem as a binary quadratic assignment problem that considers pairwise correlations between the gaps as well as the likelihoods of individual gaps, and estimates all parameters based on the Structured SVM framework so that the proposed method works well regardless of writing styles and written languages without user-defined parameters.
Abstract: Segmentation of handwritten document images into text-lines and words is an essential task for optical character recognition. However, since the features of handwritten document are irregular and diverse depending on the person, it is considered a challenging problem. In order to address the problem, we formulate the word segmentation problem as a binary quadratic assignment problem that considers pairwise correlations between the gaps as well as the likelihoods of individual gaps. Even though many parameters are involved in our formulation, we estimate all parameters based on the Structured SVM framework so that the proposed method works well regardless of writing styles and written languages without user-defined parameters. Experimental results on ICDAR 2009/2013 handwriting segmentation databases show that proposed method achieves the state-of-the-art performance on Latin-based and Indian languages.

37 citations

Proceedings ArticleDOI
18 Sep 2012
TL;DR: The proposed normalization methods for handwriting recognition and moment-based normalization of images from digit recognition to the recognition of handwritten text provide robust estimates for text characteristics such as size and position of words within an image.
Abstract: In this paper, we extend the concept of moment-based normalization of images from digit recognition to the recognition of handwritten text. Image moments provide robust estimates for text characteristics such as size and position of words within an image. For handwriting recognition the normalization procedure is applied to image slices independently. Additionally, a novel moment-based algorithm for line-thickness normalization is presented. The proposed normalization methods are evaluated on the RIMES database of French handwriting and the IAM database of English handwriting. For RIMES we achieve an improvement from 16.7% word error rate to 13.4% and for IAM from 46.6% to 40.4%.

37 citations

Proceedings ArticleDOI
30 Aug 1992
TL;DR: The main features of the so called SARAT-system are the segmentation into single characters through recognition, contour based features, statistical distance classification, and a word module.
Abstract: Presents a new system for the automatic recognition of grabic printed text. The system is still under development. Here the concept of the so called SARAT-system is presented together with some very promising first results. The main features of the system are the segmentation into single characters through recognition, contour based features, statistical distance classification, and a word module. >

37 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
86% related
Feature (computer vision)
128.2K papers, 1.7M citations
85% related
Image segmentation
79.6K papers, 1.8M citations
84% related
Convolutional neural network
74.7K papers, 2M citations
84% related
Object detection
46.1K papers, 1.3M citations
83% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202314
202241
20201
20192
20189
201751