An On-line Recognition System for Handwritten Pitman Shorthand

Ma, Yukun; Leedham, Graham; Higgins, Colin; Htwe, Swe Myo

doi:10.1109/tencon.2005.301199

Cited by 3 publications

(3 citation statements)

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“…Recognition rate has been increased by varying the number of layers in CNN and epochs during the training process. Compared to the accuracy of the PITMAN [2] recognition, the proposed method achieved 10% more. Hence this method can be employed for real time recognition of the Teeline shorthand alphabets.…”

Section: Discussionmentioning

confidence: 91%

“…Based on the implementation experience of the system, future research directions are discussed. [2] Proposed systemis capable of taking Teeline alphabet image as the input, it circulates through the eleven layers and predicts the English alphabet. Section 2 describes about the working of the network, Section 3 explains about the experimental procedure, section 4 describes about the accuracy of the system (result analysis) and next section is about the conclusion and future scope.…”

mentioning

confidence: 99%

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Recognition of Teeline Shorthand using Deep Learning.

Mr. Shivaprakash*,

Burkpalli,

Anami

2020

IJITEE

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Recognition of the Teeline shorthand is the interesting research problem. The accurate recognition of the Teeline patterns which helps the journalists to summarize the VIPs talk during the press meets which reduces the writing time. In order to address the above issues, the proposed method addresses the automatic recognition of the Teeline symbols which is used to provide alphabets as output. The framework consists of obtaining the input image, pre-processing it, perform the background subtraction on grayscale image and prepare the dataset for recognition. To prepare the dataset, the letters are written using the digital pen, it generates the pixelsand it is recorded in the Android application. Later these pixels are used to convert them into the image dataset. The prepared database is fed to the manually designed eleven layered Convolutional Neural Network (CNN). CNN is designed in such a way that it accepts the input Teeline alphabet and it able to say the equivalent English alphabet based on the training process. It has been carried out on the MATLAB 2018b and achieved the accuracy of 95%.

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Section: Discussionmentioning

confidence: 91%

mentioning

confidence: 99%

Recognition of Teeline Shorthand using Deep Learning.

Mr. Shivaprakash*,

Burkpalli,

Anami

2020

IJITEE

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“…The various cases of results show that the deep learning based algorithm is able to recognize the real time Teeline shorthand characters. Compared to the Pitman shorthand language detection accuracy[2] the proposed system had achieved 5 -8 % more accuracy.…”

mentioning

confidence: 89%

Analysis of Teeline Shorthand Recognition using Machine Learning and Deep Learning Techniques.

Mr. Shivaprakash*,

Burkpalli,

Anami

2020

IJITEE

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In order to take notes of the speech delivered by the VIPs in the short time short hand language is employed. Mainly there are two shorthand languages namely Pitman and Teeline. An automatic shorthand language recognition system is essential in order to make use of the handheld devices for speedy conversion to the original text. The paper addresses and compares the recognition of the Teeline alphabets using the Machine learning (SVM and KNN) and deep learning (CNN) techniques. The dataset has been prepared using the digital pen and the same is processed and stored using the android application. The prepared dataset is fed to the proposed system and accuracy of recognition is compared. Deep learning technique gave higher accuracy compared to machine learning techniques. MATLAB 2018b platform is used for implementation of the experimental setup.

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