130 research outputs found
Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks
Most of the methods on handwritten recognition in the literature are focused
and evaluated on Black and White (BW) image databases. In this paper we try to
answer a fundamental question in document recognition. Using Convolutional
Neural Networks (CNNs), as eye simulator, we investigate to see whether color
modalities of handwritten digits and words affect their recognition accuracy or
speed? To the best of our knowledge, so far this question has not been answered
due to the lack of handwritten databases that have all three color modalities
of handwritings. To answer this question, we selected 13,330 isolated digits
and 62,500 words from a novel Persian handwritten database, which have three
different color modalities and are unique in term of size and variety. Our
selected datasets are divided into training, validation, and testing sets.
Afterwards, similar conventional CNN models are trained with the training
samples. While the experimental results on the testing set show that CNN on the
BW digit and word images has a higher performance compared to the other two
color modalities, in general there are no significant differences for network
accuracy in different color modalities. Also, comparisons of training times in
three color modalities show that recognition of handwritten digits and words in
BW images using CNN is much more efficient
Recognition techniques for online Arabic handwriting recognition systems
Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally,
online text recognition field has been gaining more interest
lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed
Recognition of off-line printed Arabic text using Hidden Markov Models.
yesThis paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256).
Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts.
The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages
Non-english and non-latin signature verification systems: A survey
Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems
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