Hybrid HMM/BLSTM system for multi-script keyword spotting in printed and handwritten documents with identification stage

2019 ◽  
Vol 32 (13) ◽  
pp. 9201-9215
Author(s):  
Ahmed Cheikhrouhou ◽  
Yousri Kessentini ◽  
Slim Kanoun
Keyword(s):  
Keyword Spotting ◽  
Handwritten Documents ◽  
Identification Stage

scholarly journals Filters for graph-based keyword spotting in historical handwritten documents

2020 ◽  
Vol 134 ◽  
pp. 125-134 ◽  
Author(s):  
Michael Stauffer ◽  
Andreas Fischer ◽  
Kaspar Riesen
Keyword(s):  
Keyword Spotting ◽  
Handwritten Documents

Bayesian background models for keyword spotting in handwritten documents

2017 ◽  
Vol 64 ◽  
pp. 84-91 ◽  
Author(s):  
Gaurav Kumar ◽  
Venu Govindaraju
Keyword(s):  
Keyword Spotting ◽  
Handwritten Documents ◽  
Background Models

Development of a Two-Stage Segmentation-Based Word Searching Method for Handwritten Document Images

2018 ◽  
Vol 29 (1) ◽  
pp. 719-735 ◽  
Author(s):  
Samir Malakar ◽  
Manosij Ghosh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Feature Vector ◽  
Binary Classification ◽  
Research Problem ◽  
Document Image ◽  
Feature Descriptor ◽  
Keyword Spotting ◽  
Two Stage ◽  
Handwritten Documents ◽  
And Gender ◽  
Searching Method

Abstract Word searching or keyword spotting is an important research problem in the domain of document image processing. The solution to the said problem for handwritten documents is more challenging than for printed ones. In this work, a two-stage word searching schema is introduced. In the first stage, all the irrelevant words with respect to a search word are filtered out from the document page image. This is carried out using a zonal feature vector, called pre-selection feature vector, along with a rule-based binary classification method. In the next step, a holistic word recognition paradigm is used to confirm a pre-selected word as search word. To accomplish this, a modified histogram of oriented gradients-based feature descriptor is combined with a topological feature vector. This method is experimented on a QUWI English database, which is freely available through the International Conference on Document Analysis and Recognition 2015 competition entitled “Writer Identification and Gender Classification.” This technique not only provides good retrieval performance in terms of recall, precision, and F-measure scores, but it also outperforms some state-of-the-art methods.

Two-stage approach to keyword spotting in handwritten documents

2013 ◽  
Author(s):  
Mehdi Haji ◽  
Mohammad R. Ameri ◽  
Tien D. Bui ◽  
Ching Y. Suen ◽  
Dominique Ponson
Keyword(s):  
Keyword Spotting ◽  
Two Stage ◽  
Handwritten Documents

KEYWORD SPOTTING FROM ONLINE CHINESE HANDWRITTEN DOCUMENTS USING ONE-VERSUS-ALL CHARACTER CLASSIFICATION MODEL

2013 ◽  
Vol 27 (03) ◽  
pp. 1353001 ◽  
Author(s):  
HENG ZHANG ◽  
DA-HAN WANG ◽  
CHENG-LIN LIU ◽  
HORST BUNKE
Keyword(s):  
Classification Model ◽  
Experimental Comparison ◽  
Support Vector ◽  
Svm Classifier ◽  
Keyword Spotting ◽  
Handwritten Documents ◽  
Handwritten Text Recognition ◽  
Adaptive Thresholds ◽  
Query Word ◽  
Character Classification

In this paper, we propose a method for text-query-based keyword spotting from online Chinese handwritten documents using character classification model. The similarity between the query word and handwriting is obtained by combining the character classification scores. The classifier is trained by one-versus-all strategy so that it gives high similarity to the target class and low scores to the others. Using character classification-based word similarity also helps overcome the out-of-vocabulary (OOV) problem. We use a character-synchronous dynamic search algorithm to efficiently spot the query word in large database. The retrieval performance is further improved by using competing character confusion and writer-adaptive thresholds. Our experimental results on a large handwriting database CASIA-OLHWDB justify the superiority of one-versus-all trained classifiers and the benefits of confidence transformation, character confusion and adaptive thresholds. Particularly, a one-versus-all trained prototype classifier performs as well as a linear support vector machine (SVM) classifier, but consumes much less storage of index file. The experimental comparison with keyword spotting based on handwritten text recognition also demonstrates the effectiveness of the proposed method.

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