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
05 Nov 2007
TL;DR: In this paper, the use of discriminating features (aspect ratio, strokes, eccentricity, etc.) as a tool for determining the script at word level in three bilingual documents representing Kannada, Tamil and Devnagari containing English numerals, based on the observation that every text has the distinct visual appearance.
Abstract: India is a multi-lingual and multi-script country where a line of a bilingual document page may contain text words in regional language and numerals in English. For optical character recognition (OCR) of such a document page, it is necessary to identify different script forms before running an individual OCR of the scripts. In this paper, we examine the use of discriminating features (aspect ratio, strokes, eccentricity, etc,) as a tool for determining the script at word level in three bilingual documents representing Kannada, Tamil and Devnagari containing English numerals, based on the observation that every text has the distinct visual appearance. The k-nearest neighbour algorithm is used to classify the new word images. The proposed algorithm is tested on 2500 sample words with various font styles and sizes. The results obtained are quite encouraging

37 citations

Proceedings ArticleDOI
20 Oct 1993
TL;DR: A cheque processing system currently under development, based on a psychological model of the reading process for a fast reader, and the module for extracting graphical clues, implemented with the techniques of mathematical morphology, is discussed.
Abstract: A cheque processing system currently under development is described. More precisely, the cursive script recognition module for the legal amount is discussed. Commonly, systems perform recognition either on a character by character basis, or on a word level. The authors investigate the recognition at a higher level of abstraction, at the sentence level. Knowledge of context, orthography, syntax and semantics is used to supplement the information from the graphical input. The system is based on a psychological model of the reading process for a fast reader. The module for extracting graphical clues, implemented with the techniques of mathematical morphology, is discussed. >

37 citations

Proceedings ArticleDOI
01 Sep 2001
TL;DR: A system for the automatic generation of synthetic databases for the development or evaluation of Arabic word or text recognition systems (Arabic OCR) is presented and special problems caused by specific features of Arabic, such as printing from right to left, many diacritical points, variation in the height of characters, and changes in the relative position to the writing line are suggested.
Abstract: A system for the automatic generation of synthetic databases for the development or evaluation of Arabic word or text recognition systems (Arabic OCR) is presented. The proposed system works without any scanning of printed paper. Firstly Arabic text has to be typeset using a standard typesetting system. Secondly a noise-free bitmap of the document and the corresponding ground truth (GT) is automatically generated. Finally, an image distortion can be superimposed to the character or word image to simulate the expected real world noise of the intended application. All necessary modules are presented together with some examples. Special problems caused by specific features of Arabic, such as printing from right to left, many diacritical points, variation in the height of characters, and changes in the relative position to the writing line, are suggested. The synthetic data set was used to train and test a recognition system based on hidden Markov model (HMM), which was originally developed for German cursive script, for Arabic printed words. Recognition results with different synthetic data sets are presented.

37 citations

Journal ArticleDOI
TL;DR: In this paper, a gradation of pattern discrimination problems is encountered in interpreting handwritten postal addresses, including handwritten numeral recognition, alphanumeral recognition with 36 classes, and touching-digit pair recognition with 100 classes.
Abstract: A gradation of pattern discrimination problems is encountered in interpreting handwritten postal addresses. There are several multiclass discrimination problems, including handwritten numeral recognition with 10 classes, alphanumeral recognition with 36 classes, and touching-digit pair recognition with 100 classes. Word recognition with a lexicon is a problem where the number of classes varies from a few to about a thousand. Some of the discrimination techniques, particularly those with few classes, lend themselves well to neural network classification, while others are better handled by Bayesian polynomial and nearest-neighbor methods. This paper describes each of the discrimination problems and the performances of each of the subsystems in a handwritten address interpretation system developed at CEDAR.

37 citations

Journal ArticleDOI
TL;DR: A novel scene text-recognition method integrating structure-guided character detection and linguistic knowledge is proposed, using part-based tree structure to model each category of characters so as to detect and recognize characters simultaneously.
Abstract: Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text-recognition method integrating structure-guided character detection and linguistic knowledge. We use part-based tree structure to model each category of characters so as to detect and recognize characters simultaneously. Since the character models make use of both the local appearance and global structure informations, the detection results are more reliable. For word recognition, we combine the detection scores and language model into the posterior probability of character sequence from the Bayesian decision view. The final word-recognition result is obtained by maximizing the character sequence posterior probability via Viterbi algorithm. Experimental results on a range of challenging public data sets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method achieves state-of-the-art performance both for character detection and word recognition.

36 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