Automated read-write kit for blind using hidden Markov model and optical character recognition

Hidden Markov Model (HMM) is an effective method to describe sequential signals in many applications. As to model estimation issue, common training algorithm only focuses on the optimization of model parameters. However, model structure influences system performance as well. Although some structure optimization methods are proposed, they are usually implemented as an independent module before parameter optimization. In this paper, the clustering feature of states in HMM is discussed through comparing the mechanism of Quadratic Discriminant Function (QDF) classifier and HMM. Then, through the clustering effect of Viterbi training and Baum–Welch training, a novel clustering-based model pre-training approach is proposed. It can optimize model parameters and model structure by turns, until the representative states of all models are explored. Finally, the proposed approach is evaluated on two typical OCR applications, printed and handwritten Arabic text line recognition. And it is compared with some other optimization methods. The improvement of character recognition performance proves the proposed approach can make more precise state allocation. And the representative states are benefit to HMM decoding.

Download Full-text

Hidden Markov Model Based Odia Numeral Recognition Using Moment and Structural Features

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8614.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2317-2325 ◽

Cited By ~ 1

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Character Recognition ◽

Optical Character Recognition ◽

Hidden Markov ◽

Noise Removal ◽

Structural Features ◽

Hard Copy ◽

Complex Nature ◽

Model Based

Optical character recognition (OCR) is a strategy to perceive character from optically checked and digitized pages. OCR plays an important role for Indian script research. The official language of the state Odisha is Odia. OCR face an incredible difficulties to recognize Odia language due to similar shape characters, their complex nature, the complicated way in which they combine form to compound character, use of Matra etc. Each character and numbers are passed through several modules like binarization, noise removal, segmentation, line segmentation, word segmentation, skeletonization, deskewing, thinning, thickening. The input picture is standardized to a size of 50 x 50 2D pictures. HMM is a stochastic process which has utilized in various applications for example speech recognition, Handwriting recognition, Gesture recognition. In this paper we utilized HMM to recognize the Odia character and numbers. Hidden Markov Model have many advantages such as resistant to noise, handle contrast recorded as a hard copy and the HMM devices are effectively accessible. In our proposed method we have developed an efficient recognition algorithm using Hidden Markov model based on moment based and structural feature to recognize Odia characters and numerals.

Download Full-text