Offline Cursive Handwritten Word Using Hidden Markov Model Technique

Sagar, Sourabh; Dixit, Sunanda; Mahesh, Bhargavi

doi:10.1007/978-981-32-9690-9_58

Cited by 1 publication

(1 citation statement)

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“…Then, for estimation of posterior probabilities [ 3 , 13 , 15 , 16 , 17 ], classifiers such as Gaussian Mixture (GMM) or artificial neural networks (ANN) and its variants have been widely used. These probabilities are input to the Hidden Markov Model (HMM) [ 18 ] to produce transcription. HMMs have drawbacks because they are unsuccessful in modeling long-term dependencies in inputs.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Pashto Handwritten Characters Based on Deep Learning

Amin

Yasir

Ahn

2020

Sensors

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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

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