CHAIN CODE PROCESSING FOR HANDWRITTEN WORD RECOGNITION

2021 ◽  
Author(s):  
SHAKUNTHALA B S ◽  
C S PILLAI
Keyword(s):  
Word Recognition ◽  
Chain Code ◽  
Handwritten Word Recognition

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.

SEGMENTATION VERSUS NON-SEGMENTATION BASED NEURAL TECHNIQUES FOR CURSIVE WORD RECOGNITION: AN EXPERIMENTAL ANALYSIS

2002 ◽  
Vol 02 (04) ◽  
pp. 377-384 ◽  
Author(s):  
XIAOLONG FAN ◽  
BRIJESH VERMA
Keyword(s):  
Neural Network ◽  
Word Recognition ◽  
Recognition Rate ◽  
Neural Network Classifier ◽  
Extraction Techniques ◽  
Chain Code ◽  
Benchmark Database ◽  
Fair Comparison ◽  
Word Classes ◽  
Handwritten Word Recognition

This paper presents a comparative analysis of segmentation and non-segmentation based techniques for cursive handwritten word recognition. In our segmentation based technique, every word is segmented into characters, the chain code features are extracted from segmented characters, the features are fed to neural network classifier and finally the words are constructed using a string compare algorithm. In our non-segmentation based technique, the chain code features are extracted directly from words and the words are fed to a neural network classifier to classify them into word classes. To make a fair comparison, a CEDAR benchmark database is used, and the parameters such as the number of words, thresholding, resizing, feature extraction techniques, etc. are kept same for both the techniques. The experimental results and analysis show that the non-segmentation technique achieves higher recognition rate than the segmentation based technique.

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