Optical Character Recognition for Printed Tamizhi Documents using Deep Neural Networks

Munivel, Monisha; Enigo, V. S. Felix

doi:10.14429/djlit.42.4.17742

Cited by 4 publications

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“…Their use, which is not limited to any object, makes them a popular part of computer vision. OCR is often discussed in research on the detection of real objects in the form of objects or text, as is the case with automatic detection of license plate numbers [30], [31], object detection in image documents [32], handwriting detection [33], identification of ancient manuscripts [34] and many more.…”

Section: Related Workmentioning

confidence: 99%

Text Recognition for Library Collection in Different Light Conditions

Yuadi,

Sigh,

Nihaya

2024

TEM Journal

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Book arrangement problems in Indonesia mainly utilize traditional systems. However, it contributes to user frustration and dissatisfaction, leading to difficulties in the retrieval process and hindering book monitoring. Therefore, an efficient arrangement is needed to make it easier for librarians to organize collections at the library. Hence, this study proposes and evaluates Optical Character Recognition (OCR) approaches such as Tesseract-OCR and Efficient and Accurate Scene Text (EAST) by considering various light factors and device applications. An evaluation was performed to determine the effectiveness of the methods for detecting classification numbers using the Character Error Rate (CER) and Word Error Rate (WER). The results obtained can be seen from the lighting and the use of image capture tools that can affect the text detection results. The higher the lighting, the better the text detection results obtained. However, in the extraction tool using a security camera yields a better output than the others. In conclusion, the modernization of book arrangement systems by implementing OCR through security cameras could enhance user experience in the retrieval process.

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Section: Related Workmentioning

confidence: 99%

Text Recognition for Library Collection in Different Light Conditions

Yuadi,

Sigh,

Nihaya

2024

TEM Journal

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“…In addition, in [13], Sundanese writing inscribed on palm leaves was recognised using a three-layer CNN with a 73% recognition accuracy. Next, in [14], was employed for recognition, and Tesseract training was performed using a deep neural network architect. Then, CNN long-short-term memory (LSTM) networks were configured and trained for the language model of the Tamizhi script, leading to an OCR accuracy of 91.21%.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Altalhi,

Alwethinani,

Alghamdi

et al. 2023

IJACSA

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Inscriptions play an important role in preserving historical information. As such, conservation of these inscriptions provides valuable insights into the history and cultural heritage of the region. Musnad inscriptions are considered one of the earliest forms of writing from the Arabian Peninsula, preceding the modern Arabic font; however, most Musnad inscriptions remain unread and untranslated, signifying a substantial loss of historical information. In response, this paper represents a significant contribution to the field by proposing a successful approach to interpreting Musnad inscriptions. To do so, a dataset was prepared from the Saudi Arabian Ministry of Culture and subjected to preprocessing for optimal recognition, a step that entailed several experiments to enhance image quality and preparedness for recognition. The dataset was then trained and tested with 29 classes using three different convolutional neural network (CNN) architectures: Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50) and MobileNetV2. Thereafter, the performance of each architecture was evaluated based on its accuracy in recognising Musnad inscriptions. The results demonstrate that VGG16 achieved the highest accuracy of 93.81%, followed by ResNet50 at 89.39% and MobileNetV2 at 80.02%.

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“…Around 198 contemporary scripts in Central and South Asia are believed to have evolved from the Brahmi script. [2] In the Modern Tamil script stage (16th century CE to present), the Tamil characters got its current shape which are used in contemporary Tamil literature and everyday communication.…”

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confidence: 99%

Ancient Character Recognition: A Comprehensive Review

Krithiga,

Varsini,

Gabriel Joshua

et al. 2023

IEEE Access

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Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic.

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Optical Character Recognition for Printed Tamizhi Documents using Deep Neural Networks

Cited by 4 publications

References 0 publications

Text Recognition for Library Collection in Different Light Conditions

Text Recognition for Library Collection in Different Light Conditions

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Ancient Character Recognition: A Comprehensive Review

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