Online Persian Hand Writing Recognition Using Language Model and Reduction of User Writing Rules

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Maskanati Salman; KESHAVARZ AHMAD

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Journal: SIGNAL AND DATA PROCESSING Year:2017 | Volume:14 | Issue:2 (serial 32) Page(s): 3-24

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Information Journal Paper

Title

Online Persian Hand Writing Recognition Using Language Model and Reduction of User Writing Rules

Author(s)

Maskanati Salman | KESHAVARZ AHMAD | Issue Writer Certificate

Keywords

Online RecognitionQ2

Persian HandwritingQ1

k-nearest NeighborQ1

Language ModelQ1

User LimitationQ1

Abstract

The Joint-up and cursive form of Persian words and immense variety of its scripts and also different figures of Persian letters which depend on their sitting positions in the words have turned the Persian Handwritings recognition to an intense challenge. The major obstacle of most often recognition ways is their inattention to sentence contexture, causes utilization of a word with correct appearance within an incorrect sentence all when input word is misrecognized. Sketching a solution that provides suitable analysis of sentence contexture requires huge linguistic resources to takes place as a fine representative for the chosen language to be recognized. In this article, a new method for Persian words Online Recognition is presented which tries to improve recognition process by using the term contexture. Also, to reduce the limits and rules that gainers compel to submit. The recognition method demonstrated in this article includes: the symptoms and morphemes framework of input handwritten are segregated and the framework of each morpheme with its symptoms is specified at first, then the symptoms of morphemes are specified and based on them a collection of words is being considered as a hypothesis. Each hypothesis is given a score by measuring its similarity to input handwritten and according to taken scores the likely hypothesizes are indicated. Then this procedure is led to achieve more likely hypothesizes by lingual model. To totalize the scores of a hypothesis, for the reason of the differences in scale of taken scores, a method of scores normalization is being offered. The test results demonstrate that by utilization of a Language Model with an online system of handwriting recognition, a significant reduction of words recognition error rate is being achieved. In addition of error rate reduction, by taking advantage of Language Model, a technique is being offered that can handle the Persian vocabulary recognition entirely. By availing the offered manner, the recognition precision at initial stage of letters level up to 95. 9% and so the Language Model recognition up to 99. 3% improved. So using a huge linguistic resources for Persian language and utilization of a Language Model, can improved the accuracy of recognition. For furture work, reinforcement learning algorithm is suggested for adaptation the algorithm to users.

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APA: Copy

Maskanati, Salman, & KESHAVARZ, AHMAD. (2017). Online Persian Hand Writing Recognition Using Language Model and Reduction of User Writing Rules. SIGNAL AND DATA PROCESSING, 14(2 (serial 32) ), 3-24. SID. https://sid.ir/paper/160758/en

Vancouver: Copy

Maskanati Salman, KESHAVARZ AHMAD. Online Persian Hand Writing Recognition Using Language Model and Reduction of User Writing Rules. SIGNAL AND DATA PROCESSING[Internet]. 2017;14(2 (serial 32) ):3-24. Available from: https://sid.ir/paper/160758/en

IEEE: Copy

Salman Maskanati, and AHMAD KESHAVARZ, “Online Persian Hand Writing Recognition Using Language Model and Reduction of User Writing Rules,” SIGNAL AND DATA PROCESSING, vol. 14, no. 2 (serial 32) , pp. 3–24, 2017, [Online]. Available: https://sid.ir/paper/160758/en

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