Mohammad S. Khorsheed

Offline recognition of omnifont Arabic text using the HMM ToolKit (HTK)

This paper presents a cursive Arabic text recognition system. The system decomposes the document image into text line images and extracts a set of simple statistical features from a narrow window which is sliding a long that text line. It then injects the resulting feature vectors to the Hidden Markov Model Toolkit (HTK). HTK is a portable toolkit for speech recognition system. The proposed system is applied to a data corpus which includes Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts.

HMM-based system for recognizing words in historical Arabic manuscript

This paper presents an omni-font Arabic word recognition system. The system is based on multiple Hidden Markov Models (HMMs). Each word in the lexicon is represented with a distinct HMM. The proposed system first extracts a set of spectral features from word images, then uses those features to tune HMM parameters. The performance of the proposed system is assessed using a corpus that includes both handwritten and computer-generated scripts. The likelihood probability of the input pattern is calculated against each word model and the pattern is assigned to the model with the highest probability.

Fostering university–industry collaboration in Saudi Arabia through technology innovation centers

Abstract Saudi Arabia has established a goal of steering its economy away from a reliance on natural resources toward the development of knowledge-based industries. Strong collaborative relationships between research universities and private industries are central to achieving this goal. This paper proposes a new model for university–industry collaboration which targets combining academic and industrial resources to conduct research and development focused on industry-oriented problems and innovation and, additionally, educating a workforce capable of advancing national technological and economic goals. The proposed model serves as a platform for the recently established Technology Innovation Centers program at King Abdulaziz City for Science and Technology.

Mono-font cursive arabic text recognition using speech recognition system

This paper presents a system to recognise cursive Arabic typewritten text. The system is built using the Hidden Markov Model Toolkit (HTK) which is a portable toolkit for speech recognition system. The proposed system decomposes the page into its text lines and then extracts a set of simple statistical features from small overlapped windows running through each text line. The feature vector sequence is injected to the global model for training and recognition purposes. A data corpus which includes Arabic text of more than 100 A4–size sheets typewritten in Tahoma font is used to assess the performance of the proposed system.

Promoting techno-entrepreneurship through incubation: An overview at BADIR program for technology incubators

Abstract Saudi Arabia embarks the transition from conventional economy into a knowledge-based economy. This implies improving the national innovation capacity and developing an ecosystem for techno-entrepreneurs. In this regard, King Abdulaziz City for Science and Technology has established a national technology business incubation program; BADIR Program for Technology Incubators. BADIR aims to encourage non-oil based industry economic growth and foster knowledge growth and innovation-based startups. BADIR helps cultivate innovative ideas contributed by Saudi technoentrepreneurs as incubator members and enables them to scale their technology for industrialization and commercialization, and to benefit from the economic growth. This program to date has successfully assisted many technological incubators in a structured way.

Recognizing Cursive Typewritten Text Using Segmentation-Free System

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.

A markovian engine for text recognition: cursive arabic text, statistical features and interconnected HMMs

This paper presents a cursive Arabic text recognition system. The system decomposes the document image into text line images and extracts a set of simple statistical features from a one-pixel width window which is sliding a cross that text line. It then injects the resulting feature vectors to Hidden Markov Models. The proposed system is applied to a data corpus which includes Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts.