Text verification device with battery power supply

Patent metadata

Field	Value
Publication number	US-12045312-B2
Application number	US-202418601010-A
Country	US
Kind code	B2
Filing date	Mar 11, 2024
Priority date	Oct 5, 2021
Publication date	Jul 23, 2024
Grant date	Jul 23, 2024

Abstract

A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

First claim

The invention claimed is:

1. A text independent writer verification device, comprising:
a display panel configured to display hand written cursively connected Arabic words and individual hand written Arabic alphabets written by one or more target users;
a memory configured to store the hand written cursively connected Arabic words and the individual hand written Arabic alphabets;
an input device in the form of a digital pen to receive the hand written Arabic alphabets written by the one or more target users and communicatively connected to processing circuitry;
a power supply in the form of a battery connected to the input device and a processing circuitry; and
wherein the processing circuitry is configured to:
receive the dataset, the dataset including a set of hand written cursively connected Arabic words, the set of hand written cursively connected Arabic words including a minimum set of words that encompass the entire set of Arabic alphabets;
extract individual alphabets from each of the set of hand written cursively connected Arabic words to form extracted individual alphabets for the entire set of Arabic alphabets;
remove whitespace around the extracted individual alphabets;
train a deep learning Convolution Neural Network classifier with four convolution layers based on the extracted individual alphabets to form a trained deep learning classifier;
receive a new hand written cursively connected Arabic word by the target user;
perform the trained deep learning classifier to classify the target user based on the received new hand written cursively connected Arabic word; and
verify the target user from the classified target user, the verification of the target user including a verification accuracy of the hand written cursively connected Arabic words being larger than a verification accuracy threshold value;
wherein the verification accuracy (β) of each hand written cursively connected Arabic word (w) for the target user (user j ) is computed as:

β
=

∑

i

⁢

F

u
⁢
s
⁢
e
⁢

r
j

(

a
i

)

❘
"\[LeftBracketingBar]"

w

❘
"\[RightBracketingBar]"

wherein w={a 1 , a 2 , . . . , a m },
wherein for β≥0.5 the hand written cursively connected Arabic word (w) is verified to be written by the target user (user j ).

2. The device of claim 1 , wherein the processing circuitry is further configured to perform the trained deep learning classifier to classify the target user based on a target user dataset of the extracted individual alphabets; and remove one or more of the extracted individual Arabic alphabets in the target user dataset associated with the target user when an average verification error across all target users is greater than a performing threshold to form a reduced alphabets dataset, and
wherein the processing circuitry is further configured to verify the target user based on the reduced alphabets dataset.

3. The device of claim 1 , wherein the training, by the processing circuitry, includes training one deep learning Convolution Neural Network classifier for each of a plurality of target users.

4. The device of claim 2 , wherein each of the deep learning Convolution Neural Network classifiers includes a target class and a rest class.

5. The device of claim 2 , wherein the target class represents a class being associated with the target user and the rest class represents a class being associated with users excluding the target user.

6. The device of claim 1 , wherein the processing circuitry is further configured to verify the hand written cursively connected Arabic words by dividing a first number of alphabets verified to be written by the target user in the hand written Arabic words by a total number of alphabets in the hand written Arabic words.

Assignees

• Univ Prince Mohammad Bin Fahd

Inventors

• Khan Majid Ali
• Mohammad Nazeeruddin
• Brahim Ghassen Ben
• Bashar Abul
• Latif Ghazanfar

Classifications

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G06V30/293

  of characters other than Kanji, Hiragana or Katakana · CPC title

• G06V30/19147

  Obtaining sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V30/1914

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries, e.g. user dictionaries · CPC title

• G06F18/214 Primary

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title