SEGMENTATION-FREE ONLINE ARABIC HANDWRITING RECOGNITION

Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

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Offline Arabic handwriting recognition using Hidden Markov Models and Post-Recognition Lexicon Matching

2017 12th International Conference on Computer Engineering and Systems (ICCES) ◽

10.1109/icces.2017.8275310 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ahmed H. Metwally ◽

Mahmoud I. Khalil ◽

Hazem M. Abbas

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Handwriting Recognition

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Histogram of Marked Background (HMB) Feature Extraction Method for Arabic Handwriting Recognition

International Journal of Image and Graphics ◽

10.1142/s0219467822500152 ◽

2021 ◽

pp. 2250015

Author(s):

Meriem Gagaoua ◽

Hamza Ghilas ◽

Abdelkamel Tari ◽

Mohamed Cheriet

Keyword(s):

Extraction Method ◽

Markov Models ◽

Hidden Markov ◽

Handwriting Recognition ◽

Features Extraction ◽

Complex Nature ◽

Ibn Sina ◽

Feature Extraction Method ◽

Recognition Systems ◽

Background Pixel

Features extraction is one of the most important steps in handwriting recognition systems. In this paper, we propose a novel features extraction method, which is adapted to the complex nature of Arabic handwriting. The proposed feature called histogram of marked background (HMB) is not considering only ink pixels in a text image, but also uses the background of the image. Each background pixel in the text image was marked according to the repartition of ink pixels in its neighborhood. Feature vectors are extracted by computing histograms from the marked images. Hidden Markov models (HMMs) with Hidden Markov model toolkit (HTK) were used in the recognition process. The experiments were performed on two datasets: IBN SINA database of historical Arabic documents and Isolated Farsi Handwritten Character Database (IFHCDB). The proposed feature in this study produced efficient and promising results for Arabic handwriting recognition, for both isolated characters and for historical documents.

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Building Personalized Synthetic Voices for Individuals with Dysarthria using the HTS Toolkit

Computer Synthesized Speech Technologies ◽

10.4018/978-1-61520-725-1.ch006 ◽

2010 ◽

pp. 92-115 ◽

Cited By ~ 2

Author(s):

Sarah Creer ◽

Phil Green ◽

Stuart Cunningham ◽

Junichi Yamagishi

Keyword(s):

Statistical Model ◽

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Speech Communication ◽

Sufficient Information ◽

Speech Impairment ◽

New Approach ◽

Synthetic Voices ◽

The Voice

For an individual with a speech impairment, it can be necessary for them to use a device to produce synthesized speech to assist their communication. To fully support all functions of human speech communication: communication of information, maintenance of social relationships and displaying identity, the voice must be intelligible and natural-sounding. Ideally, it must also be capable of conveying the speaker’s vocal identity. A new approach based on Hidden Markov models (HMMs) has been proposed as a way of capturing sufficient information about an individual’s speech to enable a personalized speech synthesizer to be developed. This approach adapts a statistical model of speech towards the vocal characteristics of an individual. This chapter describes this approach and how it can be implemented using the HTS toolkit. Results are reported from a study that built personalized synthetic voices for two individuals with dysarthria. An evaluation of the voices by the participants themselves suggests that this technique shows promise for building personalized voices for individuals with progressive dysarthria even when their speech has begun to deteriorate.

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