An Algorithm of Bidirectional RNN for Offline Handwritten Chinese Text Recognition

Zhang, Xinfeng; Yan, Kunpeng

doi:10.1007/978-3-030-26766-7_39

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…On the other hand, Gan et, al [73] used 1-dimensional CNN for the recognition of online handwritten Chinese characters. 1-dimensional CNN seems to have performed better as recognition accuracy of [73] is 98.1% as compared to [164] where the accuracy of 83% was achieved. Zhu et, al [165] proposed a new neural network structure for Chinese handwritten character recognition.…”

Section: Chinese Languagementioning

confidence: 93%

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Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

355

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Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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Section: Chinese Languagementioning

confidence: 93%

“…During 2019 [163], [164] used techniques based on recurrent neural network(RNN) for the recognition of online and offline handwritten text, respectively. On the other hand, Gan et, al [73] used 1-dimensional CNN for the recognition of online handwritten Chinese characters.…”

Section: Chinese Languagementioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

355

View full text Add to dashboard Cite

show abstract

“…In the literature, the combination of CNN with Recurrent Neural Network (RNN) [5,6] and LSTM [7] are widely applicable for sequence modeling in HTR. However, the RNN variations face vanishing and exploding gradient problems, where the models fail to learn the long sequence information [8].…”

Section: Introductionmentioning

confidence: 99%

“…(pos,2i) = sin pos 10000 2i d model , PE (pos,2i+1) = cos pos 10000 2i d model(5) where pos and i are the position and dimension of the input, and d model is the hidden size of the Transformer model.…”

mentioning

confidence: 99%

A Pipeline Approach to Context-Aware Handwritten Text Recognition

et al. 2022

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Despite concerted efforts towards handwritten text recognition, the automatic location and transcription of handwritten text remain a challenging task. Text detection and segmentation methods are often prone to errors, affecting the accuracy of the subsequent recognition procedure. In this paper, a pipeline that locates texts on a page and recognizes the text types, as well as the context of the texts within the detected region, is proposed. Clinical receipts are used as the subject of study. The proposed model is comprised of an object detection neural network that extracts text sequences present on the page regardless of size, orientation, and type (handwritten text, printed text, or non-text). After that, the text sequences are fed to a Residual Network with a Transformer (ResNet-101T) model to perform transcription. Next, the transcribed text sequences are analyzed using a Named Entity Recognition (NER) model to classify the text sequences into their corresponding contexts (e.g., name, address, prescription, and bill amount). In the proposed pipeline, all the processes are implicitly learned from data. Experiments performed on 500 self-collected clinical receipts containing 15,297 text segments reported a character error rate (CER) and word error rate (WER) of 7.77% and 10.77%, respectively.

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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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An Algorithm of Bidirectional RNN for Offline Handwritten Chinese Text Recognition

Cited by 8 publications

References 11 publications

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

A Pipeline Approach to Context-Aware Handwritten Text Recognition

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

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