Combining Convolutional Neural Networks and LSTMs for Segmentation-Free OCR

Rawls, Stephen; Cao, Huaigu; Kumar, Senthil; Natarajan, Prem

doi:10.1109/icdar.2017.34

Cited by 24 publications

(10 citation statements)

References 18 publications

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“…The main goal of their research was to develop new principles in the field of processing handwritten text, with special emphasis on text with language specifics like diacritics. The authors of [54] presented a method believed to be simpler than existing 2D LSTM models, in particular, an end-to-end, trainable, OCR system that combines CNN for extracting features with LSTM for sequence modeling. The results were applied by the authors both to English and Arabic handwriting data and English machine print data.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification

et al. 2021

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Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes or redefinition of the production concepts is meaningful in light of the current industrial and research agendas. Both the mentioned optimization and redefinition are considered in numerous subtopics and technologies. One of the most significant topics in these areas is the newest findings and applications of artificial intelligence (AI)—machine learning (ML) and deep convolutional neural networks (DCNNs). The authors invented a method and device that supports the wiring assembly in the control cabinet production process, namely, the Wire Label Reader (WLR) industrial system. The implementation of this device was a big technical challenge. It required very advanced IT technologies, ML, image recognition, and DCNN as well. This paper focuses on an in-depth description of the underlying methodology of this device, its construction, and foremostly, the assembly industrial processes, through which this device is implemented. It was significant for the authors to validate the usability of the device within mentioned production processes and to express both advantages and challenges connected to such assembly process development. The authors noted that in-depth studies connected to the effects of AI applications in the presented area are sparse. Further, the idea of the WLR device is presented while also including results of DCNN training (with recognition results of 99.7% although challenging conditions), the device implementation in the wire assembly production process, and its users’ opinions. The authors have analyzed how the WLR affects assembly process time and energy consumption, and accordingly, the advantages and challenges of the device. Among the most impressive results of the WLR implementation in the assembly process one can be mentioned—the device ensures significant process time reduction regardless of the number of characters printed on a wire.

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Section: Literature Reviewmentioning

confidence: 99%

Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification

et al. 2021

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“…Ref. [33] suggested implementing OCR using an integrated approach combining long short-term memory (LSTM) and convolutional neural networks. The authors used the proposed technique on challenging datasets comprising handwritten images in several languages.…”

Section: Handwriting Recognition Based On Deep Learningmentioning

confidence: 99%

“…The second method is based on the CRNN with a preprocessing step that includes simple slant correction and segments lines into words using vertical and horizontal projections. Additionally, the CRNN in [33] was followed by a language model called WFST. The method in [34] is based on the CNN-FCN, which uses a pretrained model and probabilistic CER correction based on a lexicon.…”

Section: Reliability Of the Proposed Dc-crnnmentioning

confidence: 99%

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN

Al-Saffar

Awang

Al-Saiagh

et al. 2021

Sensors

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Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.

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“…Large scale data annotation is an urgent problem in the field of optical character recognition of ethnic minorities in China. Optical character recognition (OCR), as one of the first applications of deep learning, has made great progress since the emergence of convolutional neural networks [21], [22]. For example, for the recognition of Chinese characters [23], handwritten numbers [24], and text in natural scenes [25], it has realized highly satisfactory results.…”

Section: Large Scale Data Annotation In Chinese Charactersmentioning

confidence: 99%

Improvement of Ancient Shui Character Recognition Model Based on Convolutional Neural Network

et al. 2020

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This study uses deep learning theory into the character recognition technology for Shui characters in ancient books, with the objectives of overcoming the instability of the high-pixel ancient Shui characters generative model and the need for large scale handwritten text data annotation among other issues. By constructing a multilayer adversarial neural network with a Laplacian structure, a clear generative model is established for original image data of Shui characters and a stable adversarial network model with multiple mapping relationships from coarse to fine is formed. Based on the analysis of the feature distance of Shui character image samples, the minimum inter-class spacing value and the optimal number of clusters are calculated. Combined with feedback from the classifier model, the optimal number of clusters in the clustering model is adjusted, an evaluation function with information entropy adjustment and clustering threshold convergence is constructed for the unsupervised labelling of Shui character image samples. In this paper, the feedback from the convolutional neural network is used to determine the algorithmic model of the hyperparameters for clustering annotation, and this structure is also designed to improve the recognition rate of handwritten Shui characters in ancient books. INDEX TERMS Shui characters, generative adversarial network, unsupervised labeling.

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Combining Convolutional Neural Networks and LSTMs for Segmentation-Free OCR

Cited by 24 publications

References 18 publications

Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification

Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN

Improvement of Ancient Shui Character Recognition Model Based on Convolutional Neural Network

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