PHCWT: A Persian Handwritten Characters, Words and Text dataset

Montazeri, Mojtaba; Kiani, Rasoul

doi:10.1109/iranianmvip.2017.8342357

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then the preprocessed license plate character set is trained and recognized by using the proposed CNN model. The proposed method can achieve the correct recognition rate of 99.96% [5]. A database containing 90000 kinds of words is generated by artificial synthesis.…”

Section: Recognition Algorithm Based On Character/wordmentioning

confidence: 99%

Nameplate text recognition model based on attention mechanism

Wu,

Wang,

Yang

2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

View full text Add to dashboard Cite

With the accelerating speed of power grid construction, the number of substations is increasing, and the management of power equipment is becoming more and more complex. The traditional manual management method is time-consuming, labor-consuming and inefficient. Due to the complex scene of power equipment nameplate, the failure of standardization of nameplate size, many characters, miscellaneous types and many steel seal information in the nameplate, the recognition accuracy of power nameplate needs to be improved. On the basis of fully mining the principle of attention mechanism encoder and decoder, combined with LSTM and convolutional neural network, this paper proposes a nameplate text recognition model based on attention mechanism to solve the problem of automatic nameplate text recognition. Experiments show that the method proposed in this paper can effectively identify the substation nameplate.

show abstract

Section: Recognition Algorithm Based On Character/wordmentioning

confidence: 99%

Nameplate text recognition model based on attention mechanism

Wu,

Wang,

Yang

2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

View full text Add to dashboard Cite

show abstract

“…To the best of our analysis, there is no publicly available Farsi characters dataset extracted from realworld Iranian license plate images with available annotation data for classification. Although some works were performed by various researchers [21][22][23][24], they have been developed for handwritten Optical Character Recognition (OCR) researches and text-processing applications. These datasets cannot be used to recognize license plate characters due to variations of handwriting styles.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Tourani

Soroori

Shahbahrami

et al. 2021

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

View full text Add to dashboard Cite

Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.

show abstract

Persian Optical Character Recognition Using Deep Bidirectional Long Short-Term Memory

et al. 2022

View full text Add to dashboard Cite

Optical Character Recognition (OCR) is a system of converting images, including text,into editable text and is applied to various languages such as English, Arabic, and Persian. While these languages have similarities, their fundamental differences can create unique challenges. In Persian, continuity between Characters, the existence of semicircles, dots, oblique, and left-to-right characters such as English words in the context are some of the most important challenges in designing Persian OCR systems. Our proposed framework, Bina, is designed in a special way to address the issue of continuity by utilizing Convolution Neural Network (CNN) and deep bidirectional Long-Short Term Memory (BLSTM), a type of LSTM networks that has access to both past and future context. A huge and diverse dataset, including about 2M samples of both Persian and English contexts,consisting of various fonts and sizes, is also generated to train and test the performance of the proposed model. Various configurations are tested to find the optimal structure of CNN and BLSTM. The results show that Bina successfully outperformed state of the art baseline algorithm by achieving about 96% accuracy in the Persian and 88% accuracy in the Persian and English contexts.

show abstract

PHCWT: A Persian Handwritten Characters, Words and Text dataset

Cited by 3 publications

References 16 publications

Nameplate text recognition model based on attention mechanism

Nameplate text recognition model based on attention mechanism

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Persian Optical Character Recognition Using Deep Bidirectional Long Short-Term Memory

Contact Info

Product

Resources

About