A Lexicon Reduction Method Based on Clustering Word Images in Offline Farsi Handwritten Word Recognition Systems

Bayesteh, Elham; Ahmadifard, Alireza; Khosravi, Hossein

doi:10.1109/iranianmvip.2011.6121550

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…e ENIT/IFN dataset contains 32,492 word-images of Tunisian village and town names and includes five subsets, namely, a, b, c, d, and e. In order to write the vocabulary, more than 1000 writers were employed and this vocabulary entails 946 unique village and city names [58,59]. e known Iranshahr dataset includes nearly 17,000 images of handwritten names of 503 cities of Iran [60][61][62].…”

Section: Resultsmentioning

confidence: 99%

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Khosravi

Chalechale

2022

Mathematical Problems in Engineering

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Handwritten character recognition is an attractive subject in computer vision. In recent years, numerous researchers have implemented techniques to recognize handwritten characters using optical character recognition (OCR) approaches for many languages. One the most common methods to improve the OCR accuracy is based on convolutional neural networks (CNNs). A CNN model contains several kernels accompanying with pooling layers and nonlinear functions. This model overcomes the problem of adjusting the value of weights and interconnections of the neural network (NN) for creating an appropriate pipeline to process the spatial and temporal information. However, the training process of a CNN is a challenging issue. Various optimization strategies have been recently utilized for optimizing CNN’s biases and weights such as firefly algorithm (FA) and ant colony optimization (ACO) algorithms. In this study, we apply a well-known nature-inspired technique called chimp optimization algorithm (ChOA) to train a classical CNN structure LeNet-5 for Persian/Arabic handwritten recognition. The proposed method is tested on two known and publicly available handwritten word datasets. To deeply investigate and evaluate the approach, the results are compared with three optimization methods including ACO, FA, and particle swarm optimization (PSO). Outcomes indicated that the proposed ChOA technique considerably improves the performance of the original LeNet model and also shows a better performance than the others.

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Section: Resultsmentioning

confidence: 99%

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Khosravi

Chalechale

2022

Mathematical Problems in Engineering

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“…To implement the proposed method, we used the images of 200 out of 502 city names in the ‘Iranshahr’ dataset [11]. Among the city names having more than 30 samples, 200 cities were randomly selected.…”

Section: Resultsmentioning

confidence: 99%

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

Arani

Kabir

Ebrahimpour

2018

IET Computer Vision

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In this study, a method for holistic recognition of handwritten Farsi words is proposed, which fuses the outputs of right-to-left (RtL) and left-to-right (LtR) hidden Markov models (HMMs). The experimental results on 16,000 images of 200 names of Iranian cities, from the 'Iranshahr 3' are presented and compared with those methods using only RtL or LtR models. Experimental results show that the main sources of error are similar beginnings or similar endings of the words. Since RtL and LtR models when dealing with the words behave differently, there is notable error diversity between the two classifiers in such a way that their combination increases the recognition rate. Compared to the RtL-HMM, the product of output scores of the RtL and LtR-HMMs reduces the classification error to about 6, 6 and 3%, for three different feature sets. A subjective error analysis on the results is also provided.

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“…There are about 17,000 images in the database, which means that more than 30 samples are ready for each word class. The database has also been used in [30]. There are also a total of 425 sub-word classes.…”

Section: Databasementioning

confidence: 99%

Sub-word-based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network

Ghadikolaie¹,

Kabir²,

Razzazi³

2016

ETRI J

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In this paper, we present a segmentation‐based method for offline Farsi handwritten word recognition. Although most segmentation‐based systems suffer from segmentation errors within the first stages of recognition, using the inherent features of the Farsi writing script, we have segmented the words into sub‐words. Instead of using a single complex classifier with many (N) output classes, we have created N simple recurrent neural network classifiers, each having only true/false outputs with the ability to recognize sub‐words. Through the extraction of the number of sub‐words in each word, and labeling the position of each sub‐word (beginning/middle/end), many of the sub‐word classifiers can be pruned, and a few remaining sub‐word classifiers can be evaluated during the sub‐word recognition stage. The candidate sub‐words are then joined together and the closest word from the lexicon is chosen. The proposed method was evaluated using the Iranshahr database, which consists of 17,000 samples of Iranian handwritten city names. The results show the high recognition accuracy of the proposed method.

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A Lexicon Reduction Method Based on Clustering Word Images in Offline Farsi Handwritten Word Recognition Systems

Cited by 9 publications

References 11 publications

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

Sub-word-based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network

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