Efficient, Lexicon-Free OCR using Deep Learning

Namysł, Marcin; Konya, Iuliu

doi:10.1109/icdar.2019.00055

Cited by 36 publications

(25 citation statements)

References 24 publications

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“…This ANN contains 7.8×10 5 weights and has been trained on a large amount of purely synthetic data [29]. While the whole training data is not available online, the provided examples and the generation process description show that Tesseract 4.00 can be used for reference [65]. We additionally provide the results of the previous version, i.e., Tesseract OCR 3.05, as, firstly, it allows comparison with earlier studies, and, secondly, it demonstrated competitive results with other methods [61], [62].…”

Section: ) Tesseract Ocrmentioning

confidence: 99%

“…2) ABBYY FINEREADER ABBYY FineReader [66] is a state-of-the-art commercial OCR application [61] that is used in both scientific studies [7], [32], [62], [65] and business. In our experiments, we used the latest ABBYY FineReader 15.…”

Section: ) Tesseract Ocrmentioning

confidence: 99%

“…In fact, such datasets are a rarity within the modern scientific community. Firstly, private datasets are widely used [7], [62], [65] for text line recognition evaluation. Secondly, public datasets are often designed for overall document recognition systems.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

Chernyshova¹,

Sheshkus²,

Arlazarov

2020

IEEE Access

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In this paper, we introduce an ''on the device'' text line recognition framework that is designed for mobile or embedded systems. We consider per-character segmentation as a language-independent problem and individual character recognition as a language-dependent one. Thus, the proposed solution is based on two separate artificial neural networks (ANN) and dynamic programming instead of employing image processing methods for the segmentation step or end-to-end ANN. To satisfy the tight constraints on memory size imposed by embedded systems and to avoid overfitting, we employ ANNs with a small number of trainable parameters. The primary purpose of our framework is the recognition of low-quality images of identity documents with complex backgrounds and a variety of languages and fonts. We demonstrate that our solution shows high recognition accuracy on natural datasets even being trained on purely synthetic data. We use MIDV-500 and Census 1961 Project datasets for text line recognition. The proposed method considerably surpasses the algorithmic method implemented in Tesseract 3.05, the LSTM method (Tesseract 4.00), and unpublished method used in the ABBYY FineReader 15 system. Also, our framework is faster than other compared solutions. We show the language-independence of our segmenter with the experiment with Cyrillic, Armenian, and Chinese text lines.

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Section: ) Tesseract Ocrmentioning

confidence: 99%

Section: ) Tesseract Ocrmentioning

confidence: 99%

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Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

Chernyshova¹,

Sheshkus²,

Arlazarov

2020

IEEE Access

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“…Optical Character Recognition (OCR) is one of the most widely studied problems in the field of pattern recognition and computer vision [18]. Despite being widely studied, OCR remains a challenging problem when used in unconstrained environments like parking areas.…”

Section: Optical Character Recognitionmentioning

confidence: 99%

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Dalarmelina

Teixeira

Meneguette

2019

Sensors

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Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

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“…The problem of Optical Character Recognition (OCR) has been the scope of research for many years [1]- [3] due to the need for an efficient method to digitize printed documents, prevent their loss and gradual unavoidable wear, as well as increase their accessibility and portability. The challenges that face Arabic OCR systems stem from the cursive and continuous nature of Arabic scripts.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Language-Independent Multi-Font OCR for Arabic Script

Osman¹,

Zaghw²,

Hazem³

et al. 2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Optical Character Recognition (OCR) is the process of extracting digitized text from images of scanned documents. While OCR systems have already matured in many languages, they still have shortcomings in cursive languages with overlapping letters such as the Arabic language. This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. Our Arabic OCR system consists of the following modules: Pre-processing, Word-level Feature Extraction, Character Segmentation, Character Recognition, and Post-processing. This paper also proposes an improved font-independent character segmentation algorithm that outperforms the state-of-the-art segmentation algorithms. Lastly, the paper proposes a neural network model for the character recognition task. The system has experimented on several open Arabic corpora datasets with an average character segmentation accuracy 98.06%, character recognition accuracy 99.89%, and overall system accuracy 97.94% achieving outstanding results compared to the state-of-the-art Arabic OCR systems.

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Efficient, Lexicon-Free OCR using Deep Learning

Cited by 36 publications

References 24 publications

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

An Efficient Language-Independent Multi-Font OCR for Arabic Script

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