Persian printed text line detection based on font size

Fateh, Amirreza; Rezvani, Mohsen; Tajary, Alireza; Fateh, Mansoor

doi:10.1007/s11042-022-13243-x

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…In conclusion, our research focuses on advancing OCR and text detection accuracy across various services, including Thai ID cards (front and back), car registrations, mobile banking slips, book banks, passports, receipts, Indonesian ID cards (front side), and driver licenses (see the sample results in Figures 29,30,31,32,33,34,35,and 36). .…”

Section: Discussionmentioning

confidence: 99%

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Efficient Text Bounding Box Identification Using Mask R-CNN: Case of Thai Documents

Kiatphaisansophon,

Wanvarie,

Cooharojananone

2024

IEEE Access

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Section: Discussionmentioning

confidence: 99%

“…We applied preprocessing to enhance our model's performance by attempting to convert the original image from the RGB format to a binary format [36], where pixels are either black or white. This simplifies the text-detection process, making it easier to identify text regions.…”

Section: A Binarizationmentioning

confidence: 99%

Efficient Text Bounding Box Identification Using Mask R-CNN: Case of Thai Documents

Kiatphaisansophon,

Wanvarie,

Cooharojananone

2024

IEEE Access

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“…Considering text as object, the existing methodologies can be applied for text detection. This concept is mainly followed in the detection and recognition of text in different languages including English [46][47][48][49], Chinese [50][51][52], Arabic [53,54], Persian [55,56], Urdu [34,[57][58][59][60] and Hindi [61][62][63]. In some languages (including Urdu), there exists multiple ligatures and orthography.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Layer Holistic Approach for Cursive Text Recognition

et al. 2022

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Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which `Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents a methodology to recognize and classify Urdu text in Nastaleeq font, regardless of the text position in the image. The proposed solution is comprised of a two-step methodology. In the first step, text detection is performed using the Connected Component Analysis (CCA) and Long Short-Term Memory Neural Network (LSTM). In the second step, a hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) architecture is deployed to recognize the detected text. The image containing Urdu text is binarized and segmented to produce a single-line text image fed to the hybrid CNN-RNN model, which recognizes the text and saves it in a text file. The proposed technique outperforms the existing ones by achieving an overall accuracy of 97.47%.

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“…This technology is widely used in a wide range of domains, including bank check processing, postal code recognition, and the analysis of historical handwritten documents [1][2][3][4][5]. Accurate recognition of handwritten numerals is crucial in delicate systems like medical records and biometric identification, where mistakes can lead to serious consequences [6][7][8][9][10]. Nevertheless, handwritten digit recognition poses several challenges due to the diversity in shapes, sizes, and variations present in different handwriting styles.…”

Section: Introductionmentioning

confidence: 99%

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Fateh,

Birgani,

Fateh

et al. 2024

IEEE Access

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As deep learning continues to evolve, we have observed huge breakthroughs in the fields of medical imaging, video and frame generation, optical character recognition (OCR), and other domains. In the field of data analysis and document processing, the recognition of handwritten numerals plays a crucial role. This work has led to remarkable changes in OCR, historical handwritten document analysis, and postal automation. In this study, we present a novel framework to overcome this challenge, going beyond digit recognition in only one language. Unlike common methods that focus on a limited set of languages, our method provides a comprehensive solution for recognition of handwritten digit images in 12 different languages. These specific languages are chosen because most of them have fairly distant representations in latent space. We utilize transfer learning, as it reduces the computational cost and maintains the quality of enhanced images and the models' recognition accuracy. Another strength of our approach is the innovative attention-based module called the MRA module. Our experiments confirm that by applying this module, major progress is made in both image quality and the accuracy of handwritten digit recognition. Notably, we reached high precisions, surpassing nearly 2% improvement in specific languages compared to earlier techniques. In this work, we present a robust and cost-effective approach that handles multilingual handwritten numeral recognition across a wide range of languages. The code and further implementation details are available at https://github.com/CVLab-SHUT/HandWrittenDigitRecognition

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Persian printed text line detection based on font size

Cited by 5 publications

References 52 publications

Efficient Text Bounding Box Identification Using Mask R-CNN: Case of Thai Documents

Efficient Text Bounding Box Identification Using Mask R-CNN: Case of Thai Documents

A Multi-Layer Holistic Approach for Cursive Text Recognition

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

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