2 research outputs found
A Deterministic Algorithm for Arabic Character Recognition Based on Letter Properties
Handheld devices are flooding the market, and their use is becoming essential among people. Hence, the need for fast and accurate character recognition methods that ease the data entry process for users arises. There are many methods developed for handwriting character recognition especially for Latin-based languages. On the other hand, character recognition methods for Arabic language are lacking and rare. The Arabic language has many traits that differentiate it from other languages: first, the writing process is from right to left; second, the letter changes shape according to the position in the work; and third, the writing is cursive. Such traits compel to produce a special character recognition method that helps in producing applications for Arabic language. This research proposes a deterministic algorithm that recognizes Arabic alphabet letters. The algorithm is based on four categorizations of Arabic alphabet letters. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose
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A high level approach to Arabic sentence recognition
The aim of this work is to develop sentence recognition system inspired by the human reading process. Cognitive studies observed that the human tended to read a word as a whole at a time. He considers the global word shapes and uses contextual knowledge to infer and discriminate a word among other possible words. The sentence recognition system is a fully integrated system; a word level recogniser (baseline system) integrated with linguistic knowledge post-processing module. The presented baseline system is holistic word-based recognition approach characterised as probabilistic ranked task. The output of the system is multiple recognition hypotheses (N-best word lattice). The basic unit is the word rather than the character; it does not rely on any segmentation or require baseline detection. The considered linguistic knowledge to re-rank the output of the existing baseline system is the standard n-gram Statistical Language Models (SLMs). The candidates are re-ranked through exploiting phrase perplexity score. The system is an OCR system that depends on HMM models utilizing the HTK Toolkit. The baseline system supported by global transformation features extracted from binary word images. The adopted features' extraction technique is the block-based Discrete Cosine Transform (DCT) applied to the whole word image. Feature vectors extracted using block-based DCT with non-overlapping sub-block of size 8x8 pixels. The applied HMMs to the task are mono-model discrete one-dimensional HMMs (Bakis Model). A balanced actual scanned and synthetic database of word-image has been constructed to ensure an even distribution of word samples. The Arabic words are typewritten in five fonts having a size 14 points in a plain style. The statistical language models and lexicon words are extracted from The Holy Qur‟an. The systems are applied on word images with no overlap between the training and testing datasets. The actual scanned database is used to evaluate the word recogniser. The synthetic database is a large amount of data acquired for a reliable training of sentence recognition systems. This word recogniser evaluated in mono-font and multi-font contexts. The two types of word recogniser have been used to achieve a final recognition accuracy of99.30% and 73.47% in mono-font and multi-font, respectively. The achieved average accuracy by the sentence recogniser is 67.24% improved to 78.35% on average when using 5-gram post-processing. The complexity and accuracy of the post-processing module are evaluated and found that 4-gram is more suitable than 5-gram; it is much faster at an average improvement of 76.89%