Offline Persian Handwriting Recognition with CNN and RNN-CTC

Safarzadeh, Vahid Mohammadi; Jafarzadeh, Pourya

doi:10.1109/csicc49403.2020.9050073

Cited by 33 publications

(20 citation statements)

References 57 publications

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“…As can be see, compared to IRANSHAHR, much fewer investigations have been conducted on Sadri dataset. As far as the authors are concerned, there are currently only studies based on Sadri dataset [28,29]. The proposed method has several advantages over the existing methods.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…For example, the proposed method provides better results with fewer weights than the other methods. Moreover, compared to the wotk in literature [28], in which CNN was conducted for extracting a feature sequences and the RNN was used along with CTC for sequence labeling, the proposed method is end-to-end, meaning that feature extraction and classification are conducted automatically. Given the large size of trainable parameters, the method of Bonyani et al [29] used data augmentation for data generation.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…These families of neural network, which fuse classification and feature extraction tasks, are intended to recognize images based on their scale, shift, and distortions. Safarzadeh et al [28] proposed a novel approach according to the sequence labeling with CNN and Bidirectional Long Short Term Memory (LSTM). In this method, in order to ignore the segmentation step in segmentation-based methods, a Connectionist Temporal Classification (CTC) is used as the cost function.…”

Section: Literature Reviewmentioning

confidence: 99%

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Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Zohrevand

Imani

2021

IJE

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Due to the cursive-ness and high variability of Persian script, the segmentation of handwritten words into sub-words is still a challenging task. These issues could be addressed in a holistic approach by sidestepping segmentation at the character level. In this paper, an end-to-end holistic method based on deep convolutional neural network is proposed to recognize off-line Persian handwritten words. The proposed model uses only five convolutional layers and two fully connected layers for classifying word images effectively, which can lead to a substantial reduction in the required parameters. The effect of various pooling strategies is also investigated in this paper. The primary goal of this article is to ignore handcrafted feature extraction and to attain a generalized and stable word recognition system. The presented model is assessed using two famous handwritten Persian word databases called Sadri and IRANSHAHR. The recognition accuracies were obtained at 98.6% and 94.6%, on Sadri and IRANSHAHR datasets respectively, and outperformed the state-of-the-art methods.

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Section: Discussion and Comparisonmentioning

confidence: 99%

Section: Discussion and Comparisonmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Zohrevand

Imani

2021

IJE

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“…In CRNNs, the convolutional neural networks (CNNs) learn spatial and temporal information of characters within text images with less computational cost compared to MDRNNs. The state-of-the-art (SOTA) in many handwritten or printed text recognition tasks is the use of deep learning models consisting of CNNs as feature extractor and RNNs for sequence encoding by utilizing CTC as a loss function [9][10][11][12]. In literature, attention network mechanisms and transfer learning techniques were used to enhance the performance of CRNN based models [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

AHWR-Net: offline handwritten amharic word recognition using convolutional recurrent neural network

2021

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Amharic ("Image missing") is the official language of the Federal Government of Ethiopia, with more than 27 million speakers. It uses an Ethiopic script, which has 238 core and 27 labialized characters. It is a low-resourced language, and a few attempts have been made so far for its handwritten text recognition. However, Amharic handwritten text recognition is challenging due to the very high similarity between characters. This paper presents a convolutional recurrent neural networks based offline handwritten Amharic word recognition system. The proposed framework comprises convolutional neural networks (CNNs) for feature extraction from input word images, recurrent neural network (RNNs) for sequence encoding, and connectionist temporal classification as a loss function. We designed a custom CNN model and compared its performance with three different state-of-the-art CNN models, including DenseNet-121, ResNet-50 and VGG-19 after modifying their architectures to fit our problem domain, for robust feature extraction from handwritten Amharic word images. We have conducted detailed experiments with different CNN and RNN architectures, input word image sizes, and applied data augmentation techniques to enhance performance of the proposed models. We have prepared a handwritten Amharic word dataset, HARD-I, which is available publicly for researchers. From the experiments on various recognition models using our dataset, a WER of 5.24 % and CER of 1.15 % were achieved using our best-performing recognition model. The proposed models achieve a competitive performance compared to existing models for offline handwritten Amharic word recognition.

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“…As per the study, CNN shows better results than DBN in case of image recognition. For handwritten Persian word recognition, CNN and RNN along with the segmentation tool Connectionist Temporal Classification (CTC) are experimented and show good results (Safarzadeh and Jafarzadeh, 2020). Binary LSTM based deep network is used for English (Breuel, 2017) and Odia text recognition (Ray et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Deep Learning Networks on Printed Odia Characters

Pattanayak¹,

Pradhan²,

Malik³

2020

Journal of Computer Science

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Deep machine learning includes a series of layers to mimic the working of the human brain for taking a decision. Deep learning networks have shown good results in character recognition in the past. This paper evaluates the performance of different deep learning networks like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) based recurrent neural network and Convolutional LSTM on printed Odia characters. The Odia character database contains more than 24,000 images of printed Odia characters (including simple as well as complex characters) out of which 23,857 nos. of images are chosen for this work. Besides these three, a nested Convolutional neural network model is developed for different categories of printed character image groups which are formed based on their writing style. Here, in this study, the nested model is showing the best results in terms of error rate, accuracy and no. of epochs in comparison to the other three. Different pre-processing steps like binarization, sizenormalization, blurring, interpolation, etc. are involved before passing the images to the deep neural networks to increase the recognition accuracy.

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Offline Persian Handwriting Recognition with CNN and RNN-CTC

Cited by 33 publications

References 57 publications

Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

AHWR-Net: offline handwritten amharic word recognition using convolutional recurrent neural network

Performance Evaluation of Deep Learning Networks on Printed Odia Characters

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