Importance and challenges of handwriting recognition with the implementation of machine learning techniques: a survey
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Cited by 3 publications
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An intelligent character segmentation system coupled with deep learning based recognition for the digitization of ancient Tamil palm leaf manuscripts
Palm-leaf manuscripts, rich with ancient knowledge in areas such as history, art, and medicine, are vital cultural treasures, making their digitization essential for preserving this heritage. Digitization of these organic and fragile manuscripts is required to safeguard the essential ancient data. This requires optimal character segmentation and recognition algorithms. A limited number of studies have been carried out in Tamil character recognition in literature. Handling row-overlapped characters, noise introduced due to lightning issues, and dirt, as well as the removal of punch holes, auto-cropping the content, filtering out noisy or improper segmentation, etc. are the essential concerns carried out in our proposed work. This work is executed as a four-step process (1) Palm Leaf Manuscript Acquisition (2) Pre-Processing (3) Segmentation of Tamil Characters and (4) Tamil Character Recognition. During acquisition, the scanners are used for recording palm leaf manuscripts from the Tamil Nadu-oriented manuscript library. In the Pre-processing step, the Fast Non-Local Means (Fast-NLM) method, paired with median filtering is used for Denoising the scanner output image. Later, the pixels that make the characters and borders (i.e., the foreground) are identified using Sauvola thresholding. The proposed methodology introduces efficient techniques to remove Punch hole impressions from the pre-processed image, and to crop the written content from the edges. After pre-processing, the Segmentation of Tamil Characters is performed as a three-step process (a) Manuscript (b) Line, and (c) character segmentation, which addresses conjoined lines, partially/completely empty segmentations that are not previously addressed by existing techniques. This work introduces an Augmented HPP line-splitting algorithm that accurately segments written lines, handling wrong segmentation cases that were previously not considered by existing techniques. The system achieves an average segmentation accuracy of 98.25%, which far outperforms existing techniques. It also proposes a novel Punch hole removal algorithm that can locate and remove the punch-hole impressions in the manuscript image. This algorithm, along with the automated content cropping technique, increases recognition accuracy and eliminates any manual labor needed. These features make the proposed methodology highly suitable for real-time archaeological and historical researches that include manuscripts. All 247 letters and 12 numeric digits are analyzed and separated into 125 distinct writable characters. In our work, characters are segmented and used for recognition of all 247 letters and 12 digits in Tamil using a multi-class CNN with 125 classes, which drastically reduces the complexity of the neural network compared to having 257 output nodes. It offered a notable performance of 96.04% accuracy. As compared with existing Tamil and other character recognitions, this work is effective in essence of considering real-time images and the increased number of characters used.
An intelligent character segmentation system coupled with deep learning based recognition for the digitization of ancient Tamil palm leaf manuscripts
Palm-leaf manuscripts, rich with ancient knowledge in areas such as history, art, and medicine, are vital cultural treasures, making their digitization essential for preserving this heritage. Digitization of these organic and fragile manuscripts is required to safeguard the essential ancient data. This requires optimal character segmentation and recognition algorithms. A limited number of studies have been carried out in Tamil character recognition in literature. Handling row-overlapped characters, noise introduced due to lightning issues, and dirt, as well as the removal of punch holes, auto-cropping the content, filtering out noisy or improper segmentation, etc. are the essential concerns carried out in our proposed work. This work is executed as a four-step process (1) Palm Leaf Manuscript Acquisition (2) Pre-Processing (3) Segmentation of Tamil Characters and (4) Tamil Character Recognition. During acquisition, the scanners are used for recording palm leaf manuscripts from the Tamil Nadu-oriented manuscript library. In the Pre-processing step, the Fast Non-Local Means (Fast-NLM) method, paired with median filtering is used for Denoising the scanner output image. Later, the pixels that make the characters and borders (i.e., the foreground) are identified using Sauvola thresholding. The proposed methodology introduces efficient techniques to remove Punch hole impressions from the pre-processed image, and to crop the written content from the edges. After pre-processing, the Segmentation of Tamil Characters is performed as a three-step process (a) Manuscript (b) Line, and (c) character segmentation, which addresses conjoined lines, partially/completely empty segmentations that are not previously addressed by existing techniques. This work introduces an Augmented HPP line-splitting algorithm that accurately segments written lines, handling wrong segmentation cases that were previously not considered by existing techniques. The system achieves an average segmentation accuracy of 98.25%, which far outperforms existing techniques. It also proposes a novel Punch hole removal algorithm that can locate and remove the punch-hole impressions in the manuscript image. This algorithm, along with the automated content cropping technique, increases recognition accuracy and eliminates any manual labor needed. These features make the proposed methodology highly suitable for real-time archaeological and historical researches that include manuscripts. All 247 letters and 12 numeric digits are analyzed and separated into 125 distinct writable characters. In our work, characters are segmented and used for recognition of all 247 letters and 12 digits in Tamil using a multi-class CNN with 125 classes, which drastically reduces the complexity of the neural network compared to having 257 output nodes. It offered a notable performance of 96.04% accuracy. As compared with existing Tamil and other character recognitions, this work is effective in essence of considering real-time images and the increased number of characters used.
A Gateway API-Based Data Fusion Architecture for Automated User Interaction with Historical Handwritten Manuscripts
To preserve handwritten historical documents, libraries are choosing to digitize them, ensuring their longevity and accessibility. However, the true value of these digitized images lies in their transcription into a textual format. In recent years, various tools have been developed utilizing both traditional and AI-based models to address the challenges of deciphering handwritten texts. Despite their importance, there are still several obstacles to overcome, such as the need for scalable and modular solutions, as well as the ability to cater to a continuously growing user community autonomously. This study focuses on introducing a new information fusion architecture, specifically highlighting the Gateway API. Developed as part of the μDoc.tS research program, this architecture aims to convert digital images of manuscripts into electronic text, ensuring secure and efficient routing of requests from front-end applications to the back end of the information system. The validation of this architecture demonstrates its efficiency in handling a large volume of requests and effectively distributing the workload. One significant advantage of this proposed method is its compatibility with everyday devices, eliminating the need for extensive computational infrastructures. It is believed that the scalability and modularity of this architecture can pave the way for a unified multi-platform solution, connecting diverse user environments and databases.
Mapping the Knowledge Structure of Image Recognition in Cultural Heritage: A Scientometric Analysis Using CiteSpace, VOSviewer, and Bibliometrix
The application of image recognition techniques in the realm of cultural heritage represents a significant advancement in preservation and analysis. However, existing scholarship on this topic has largely concentrated on specific methodologies and narrow categories, leaving a notable gap in broader understanding. This study aims to address this deficiency through a thorough bibliometric analysis of the Web of Science (WoS) literature from 1995 to 2024, integrating both qualitative and quantitative approaches to elucidate the macro-level evolution of the field. Our analysis reveals that the integration of artificial intelligence, particularly deep learning, has significantly enhanced digital documentation, artifact identification, and overall cultural heritage management. Looking forward, it is imperative that research endeavors expand the application of these techniques into multidisciplinary domains, including ecological monitoring and social policy. Additionally, this paper examines non-invasive identification methods for material classification and damage detection, highlighting the role of advanced modeling in optimizing the management of heritage sites. The emergence of keywords such as ‘ecosystem services’, ‘models’, and ‘energy’ in the recent literature underscores a shift toward sustainable practices in cultural heritage conservation. This trend reflects a growing recognition of the interconnectedness between heritage preservation and environmental sciences. The heightened awareness of environmental crises has, in turn, spurred the development of image recognition technologies tailored for cultural heritage applications. Prospective research in this field is anticipated to witness rapid advancements, particularly in real-time monitoring and community engagement, leading to the creation of more holistic tools for heritage conservation.
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