scholarly journals Character Degradation Model and HMM Word Recognition System for Text Extracted from Maps

10.5772/17814 ◽  
2011 ◽  
Author(s):  
Aria Pezeshk ◽  
Richard L.
Keyword(s):  
Word Recognition ◽  
Recognition System ◽  
Degradation Model

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Word recognition in aphasia

2007 ◽  
pp. 140-156
Author(s):  
Sheila Blumstein
Keyword(s):  
Word Recognition ◽  
Language Processing ◽  
Speech Processing ◽  
Current Knowledge ◽  
Recognition System ◽  
Speech And Language ◽  
Neural Basis ◽  
Sentential Context ◽  
The Face ◽  
Speech And Language Processing

This article reviews current knowledge about the nature of auditory word recognition deficits in aphasia. It assumes that the language functioning of adults with aphasia was normal prior to sustaining brain injury, and that their word recognition system was intact. As a consequence, the study of aphasia provides insight into how damage to particular areas of the brain affects speech and language processing, and thus provides a crucial step in mapping out the neural systems underlying speech and language processing. To this end, much of the discussion focuses on word recognition deficits in Broca's and Wernicke's aphasics, two clinical syndromes that have provided the basis for much of the study of the neural basis of language. Clinically, Broca's aphasics have a profound expressive impairment in the face of relatively good auditory language comprehension. This article also considers deficits in processing the sound structure of language, graded activation of the lexicon, lexical competition, influence of word recognition on speech processing, and influence of sentential context on word recognition.

Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

Online Handwritten Gujarati Word Recognition

2019 ◽  
Vol 9 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Vishal A. Naik ◽  
Apurva A. Desai
Keyword(s):  
Word Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Post Processing ◽  
Mapping Rule ◽  
Hybrid Features ◽  
Rule Based ◽  
Chain Code ◽  
Handwritten Word Recognition ◽  
Gujarati Language

In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.

The use of a rapid priming technique I: Adult language processing

1999 ◽  
Vol 10 (1) ◽  
pp. 85-91
Author(s):  
Chris Davis ◽  
Anne Castles
Keyword(s):  
Word Recognition ◽  
Incomplete Information ◽  
Language Processing ◽  
Word Processing ◽  
Recognition System ◽  
Masked Priming ◽  
Written Language ◽  
Psycholinguistic Research ◽  
Skilled Readers ◽  
Priming Technique

ABSTRACTThis paper discusses the background and use of the masked priming procedure in adult psycholinguistic research. Using this technique, we address the issue of how precise the letter and word processing systems of adults is for rapidly displayed stimuli. Data is reviewed that suggests that, for skilled readers, the letter and word recognition system is sensitively tuned to the discrimination demands imposed on it by the properties of the written language. That is, the recognition system is able to be discriminative where precision is required, but is also able to consider and use incomplete information when this is predictive.

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