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.
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18 Aug 1997
TL;DR: Experimental results show that when the characters are segmented from words and are randomly presented, the accuracy of the machine recognition is comparable with the average human recognition accuracy.
Abstract: Handwritten character recognition by human readers, a statistical classifier, and a neural network is compared to know the required accuracy for handwritten word recognition. Sample characters extracted from postal address words on mail pieces collected by USPS were used to evaluate human and machine performance. Experimental results show that: 1) when the characters are segmented from words and are randomly presented, the accuracy of the machine recognition is comparable with the average human recognition accuracy, 2) the neural network employing the feature vector of size 64 outperforms the statistical classifier employing the same feature vector, and that 3) the statistical classifier employing the feature vector of size 400 achieves comparable recognition rate with the best human reader.
35 citations
11 Aug 2002
TL;DR: Two innovative techniques that contribute to the high efficiency in recognition of the mixed Chinese/English text line are presented, including a progressive search strategy based on character verification and a tree-based fast match technique with a confidence-guided adaptive stopping mechanism.
Abstract: In the past several years, we have been developing a high performance OCR engine for machine printed Chinese/English documents. We present two innovative techniques that contribute to the high efficiency in recognition of the mixed Chinese/English text line. They are (1) a progressive search strategy based on character verification, and (2) a tree-based fast match technique with a confidence-guided adaptive stopping mechanism. The efficacy of the proposed techniques is confirmed by experiments in a benchmark test.
35 citations
31 Aug 2005
TL;DR: This work proposes to build a structure tree of the text line, whose nodes represent possible word candidates, and shows that the new method can yield significant improvements over conventional word extraction methods.
Abstract: Word extraction from handwritten text lines usually involves the calculation of a line specific threshold which separates the gaps between words from the gaps inside the words in that line. We show that this approach can be improved if the decision about a gap is not only made in terms of a threshold, but also depends on the context of that gap, i.e. if the relative sizes of the surrounding gaps are taken into consideration. For this purpose, we propose to build a structure tree of the text line, whose nodes represent possible word candidates. Such a tree is traversed in a top-down manner to find the nodes that correspond to words of the text line. Experiments with different gap metrics as well as threshold types show that the new method can yield significant improvements over conventional word extraction methods.
35 citations
Journal Article•
TL;DR: This paper discusses the form image registration technique and the image masking and image improvement techniques implemented in the ICR system as part of the character image extraction process, which help in preparing the input character image for the neural networks-based classifiers.
Abstract: A Form-based Intelligent Character Recognition (ICR) System for handwritten forms, besides others, includes functional components for form registration, character image extraction and character image classification. Needless to say, the classifier is a very important component of the ICR system. Automatic recognition and classification of handwritten character images is a complex task. Neural Networks based classifiers are now available. These are fairly accurate and demonstrate a significant degree of generalisation. However any such classifier is highly sensitive to the quality of the character images given as input. Therefore it is essential that the preprocessing components of the system, form registration and character image extraction, are well designed. In this paper we discuss the form image registration technique and the image masking and image improvement techniques implemented in our system as part of the character image extraction process. These simple yet effective techniques help in preparing the input character image for the neural networks-based classifiers and go a long way in improving overall system accuracy. Although these algorithms have been discussed with reference to our ICR system they are generic in their applicability and may find use in other scenarios as well.
35 citations
10 Jul 2014
TL;DR: A review of the OCR history and the various techniques used for OCR development in the chronological order is being done.
Abstract: Many researches are going on in the field of optical character recognition (OCR) for the last few decades and a lot of articles have been published. Also a large number of OCR is available commercially. In this literature a review of the OCR history and the various techniques used for OCR development in the chronological order is being done.
35 citations