A Mobile Application for Offline Handwritten Character Recognition

Zin, Thi Thi; Pwint, Moe; Thant, Shin

doi:10.1109/gcce50665.2020.9291735

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…With the development of computer vision technology, image recognition has been widely used in various fields (Shen & He, 2011; Zin & Otsuzuki, 2020). The field of individual identification of dairy cows has also begun to use image recognition methods for individual identification (Kumar & Singh, 2019; Sun et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Individual identification of dairy cows based on deep learning and feature fusion

Kou

et al. 2022

Animal Science Journal

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Individual identification of dairy cows is one of the most important prerequisites for an intelligent dairy farming. Herein, a new method of individual identification of dairy cows based on the fusion of deep and shallow features of dairy cow's trunk image is proposed. Our aim is to obtain low-cost, touch-free, and high-accuracy individual identification of dairy cows. First, the model of VGG-16 is improved. Second, the multiple overlapping pooling layer in the improved VGG-16 model is used as a feature extractor to extract the features of dairy cow's trunk image, and the global average pooling (GAP) layer was used to replace the traditional flatten layer to reduce the dimensions of the extracted shallow and deep features. Finally, the features after the dimensionality reduction are fused using the Concat method and input the fusion of new features into the support vector machine (SVM) classifier for classification. The proposed method was trained and tested on the dataset of 3151 cow trunk images captured by us. Experimental results showed that this method not only has better recognition accuracy of 99.48% than the VGG-16, AlexNet, and ResNet-50 models but also has fewer model parameters, faster convergence speed, and stronger generalization ability.

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Section: Introductionmentioning

confidence: 99%

Individual identification of dairy cows based on deep learning and feature fusion

Kou

et al. 2022

Animal Science Journal

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“…In this context, many systems and mobile applications have been developed to perceive and comprehend their visual surroundings using handwritten text recognition techniques [2][3][4][5]. The goal of these systems is to read street signs and allow easier automated navigation [6,7], search and index a large number of images or videos on the Internet [8][9][10], detect product labels for autonomous stores [11] or help in real-time text recognition of translations on smartphones [12].…”

Section: Introductionmentioning

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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Offline handwritten character recognition with nomograph-based IMVO feature mining with DSRNN-MaxEnt classification

Tripathi,

Patheja

2023

Sādhanā

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A Mobile Application for Offline Handwritten Character Recognition

Cited by 6 publications

References 2 publications

Individual identification of dairy cows based on deep learning and feature fusion

Individual identification of dairy cows based on deep learning and feature fusion

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN

Offline handwritten character recognition with nomograph-based IMVO feature mining with DSRNN-MaxEnt classification

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