Open AccessWord Spotting in Handwritten Document Images based on Multiple Features
Author(s) -
Mallikarjun Hangarge,
C. Veershetty
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2625.1081219
Subject(s) - spotting , computer science , scripting language , artificial intelligence , word (group theory) , pattern recognition (psychology) , gabor filter , natural language processing , histogram , texture (cosmology) , keyword spotting , speech recognition , image (mathematics) , operating system , philosophy , linguistics
This paper presents word spotting in handwritten documents based on multiple features. Multiple features are derived using Gabor, Histogram oriented gradient (HOG), Local binary pattern, texture filters and Morphological filters. The real time documents are heterogeneous in nature, for instance application forms, postal cards, railway reservations forms etc. includes handwritten and printed text with different scripts. To spot a word in such documents and retrieving them from a huge digitized repository is a challenging task. To address such issues word spotting based on multiple features is carried out with learning and without learning methods. In both the methods (learning and learning free) texture filters are exhibiting outstanding performance in terms of precision recall and f-measures. To confirm the capability of the proposed method, extensive experiments are made on publically available dataset i.e.GW20 and noted encouraging results compared to other contemporary works