Results 1 - 10
of
59
Offline recognition of unconstrained handwritten texts using HMMs and statistical language models
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2004
"... This paper presents a system for the offline recognition of large vocabulary unconstrained handwritten texts. The only assumption made about the data is that it is written in English. This allows the application of Statistical Language Models in order to improve the performance of our system. Severa ..."
Abstract
-
Cited by 84 (11 self)
- Add to MetaCart
(Show Context)
This paper presents a system for the offline recognition of large vocabulary unconstrained handwritten texts. The only assumption made about the data is that it is written in English. This allows the application of Statistical Language Models in order to improve the performance of our system. Several experiments have been performed using both single and multiple writer data. Lexica of variable size (from 10,000 to 50,000 words) have been used. The use of language models is shown to improve the accuracy of the system (when the lexicon contains 50,000 words, error rate is reduced by ∼50 % for single writer data and by ∼25 % for multiple writer data). Our approach is described in detail and compared with other methods presented in the literature to deal with the same problem. An experimental setup to correctly deal with unconstrained text recognition is proposed.
Artificial Neural Networks for Document Analysis and Recognition
- IEEE TPAMI
, 2003
"... Artificial neural networks have been extensively applied to document analysis and recogni-tion. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document pro-cessing tasks like pre-processi ..."
Abstract
-
Cited by 33 (5 self)
- Add to MetaCart
(Show Context)
Artificial neural networks have been extensively applied to document analysis and recogni-tion. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document pro-cessing tasks like pre-processing, layout analysis, character segmentation, word recognition, and signature verification have been effectively faced with very promising results. This paper surveys most significant problems in the area of off-line document image processing where connectionist-based approaches have been applied. Similarities and differences between ap-proaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of the prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts most promising research guidelines in the field. In particular, a sec-ond generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.
A SVM-based cursive character recognizer
- Pattern Recognition
, 2007
"... Abstract This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines (SVMs) and a neural gas. The neural gas is used to verify ..."
Abstract
-
Cited by 17 (0 self)
- Add to MetaCart
(Show Context)
Abstract This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines (SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition.
Towards Automatic Video-based Whiteboard Reading
- INT. JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION
, 2005
"... As whiteboards have become a popular tool in meeting rooms, there has been a growing interest in making use of the whiteboard as a user interface for human computer interaction. Therefore, systems based on electronic whiteboards have been developed in order to serve as meeting assistants for e.g. co ..."
Abstract
-
Cited by 16 (4 self)
- Add to MetaCart
As whiteboards have become a popular tool in meeting rooms, there has been a growing interest in making use of the whiteboard as a user interface for human computer interaction. Therefore, systems based on electronic whiteboards have been developed in order to serve as meeting assistants for e.g. collaborative working. However, as special pens and erasers are required, the natural interaction is restricted. In order to render this communication method more natural it was proposed to retain ordinary whiteboard and pens and to visually observe the writing process using a video camera [11, 9]. In this paper a prototype system for automatic video-based whiteboard reading is presented. The system is designed for recognizing unconstrained handwritten text and is further characterized by an incremental processing strategy in order to facilitate recognizing portions of text as soon as they have been written on the board. We will present the methods employed for extracting text regions, pre-processing, feature extraction, and statistical modeling and recognition. Evaluation results on a writer independent unconstrained handwriting recognition task demonstrate the feasibility of the proposed approach.
Representations and Metrics for Off-Line Handwriting Segmentation
, 2002
"... Segmentation is a key step in many off-line handwriting recognition systems but, to date, there are almost no ground truth segmentation databases and no widely accepted and formally defined metrics for segmentation performance. This paper proposes a representation of segmentations and presegmentatio ..."
Abstract
-
Cited by 13 (5 self)
- Add to MetaCart
Segmentation is a key step in many off-line handwriting recognition systems but, to date, there are almost no ground truth segmentation databases and no widely accepted and formally defined metrics for segmentation performance. This paper proposes a representation of segmentations and presegmentations in terms of color images. Such representations allow convenient interchange of ground truth and hypothesized segmentations in the form of standard image formats. The paper formally defines the notions of oversegmentation and undersegmentation in terms of the maximal bipartite match between corresponding pixels. It also defines a number of metrics that quantify the frequency and extent of events in handwriting like kerning, splitting, and merging of characters. It is hoped that these metrics and representations will find wider use in the community and serve as a basis for creating standard training and test databases of segmentation data.
Dynamic Time Warping: An intuitive way of handwriting recognition?
, 2004
"... Automatic handwriting recognition has had the interest of researchers for decades. Although there are various applications for which the technique is already used in daily life, a number of problems still has to be solved. At this moment, one of the biggest problems is the low user acceptance: the s ..."
Abstract
-
Cited by 12 (2 self)
- Add to MetaCart
Automatic handwriting recognition has had the interest of researchers for decades. Although there are various applications for which the technique is already used in daily life, a number of problems still has to be solved. At this moment, one of the biggest problems is the low user acceptance: the systems are not accurately enough, and moreover, the mistakes that recognizers make are usually not very understandable to humans, which can frustrate the users of the systems. This thesis
Rejection Strategies for Handwritten Word Recognition
, 2004
"... In this paper, we investigate different rejection strategies to verify the output of a handwriting recognition system. We evaluate a variety of novel rejection thresholds including global, class--dependent and hypothesis--dependent thresholds to improve the reliability in recognizing unconstrained h ..."
Abstract
-
Cited by 9 (2 self)
- Add to MetaCart
In this paper, we investigate different rejection strategies to verify the output of a handwriting recognition system. We evaluate a variety of novel rejection thresholds including global, class--dependent and hypothesis--dependent thresholds to improve the reliability in recognizing unconstrained handwritten words. The rejection thresholds are applied in a post--processing mode to either reject or accept the output of the handwriting recognition system which consists of a list with the N--best word hypotheses. Experimental results show that the best rejection strategy is able to improve the reliability of the handwriting recognition system from about 78% to 94% while rejecting 30% of the word hypotheses.
Recognition and Verification of Unconstrained Handwritten Words
, 2005
"... This paper presents a novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system. Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of text transcripts, segm ..."
Abstract
-
Cited by 8 (1 self)
- Add to MetaCart
This paper presents a novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system. Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of text transcripts, segmentation boundaries of the word hypotheses into characters, and recognition scores. The verification consists of an estimation of the probability of each segment representing a known class of character. Then, character probabilities are combined to produce word confidence scores which are further integrated with the recognition scores produced by the recognition system. The N-best recognition hypothesis list is reranked based on such composite scores. In the end, rejection rules are invoked to either accept the best recognition hypothesis of such a list or to reject the input word image. The use of the verification approach has improved the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process. Our approach is described in detail and the experimental results on a large database of unconstrained handwritten words extracted from postal envelopes are presented.
Offline Cursive Handwriting Recognition System based on Hybrid Markov Model and Neural Networks
"... An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segment ..."
Abstract
-
Cited by 6 (1 self)
- Add to MetaCart
(Show Context)
An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1). character-level training 2). word-level training. The recognition performances of the two systems are discussed. I.
Preprocessing phase for Arabic Word Handwritten Recognition
, 2006
"... Abstract—In this paper we reviewed the importance of the pattern classification and its application. We list the characteristics of Arabic language writing style, furthermore focused on the preprocessing step of the recognition system. We described and tested algorithm to create skeleton which will ..."
Abstract
-
Cited by 6 (0 self)
- Add to MetaCart
(Show Context)
Abstract—In this paper we reviewed the importance of the pattern classification and its application. We list the characteristics of Arabic language writing style, furthermore focused on the preprocessing step of the recognition system. We described and tested algorithm to create skeleton which will be the base representation of Arabic words which we will use for feature extraction phase. Also we discussed and implemented the algorithm of baseline detection. The algorithms of skeleton and baseline detection are tested using database IFN/ENIT of handwritten Tunisian town names and they work properly. 1.
