67 research outputs found
Recognizing Degraded Handwritten Characters
In this paper, Slavonic manuscripts from the 11th
century written in Glagolitic script are
investigated. State-of-the-art optical character recognition methods produce poor results
for degraded handwritten document images. This is largely due to a lack of suitable
results from basic pre-processing steps such as binarization and image segmentation.
Therefore, a new, binarization-free approach will be presented that is independent of
pre-processing deficiencies. It additionally incorporates local information in order to
recognize also fragmented or faded characters. The proposed algorithm consists of
two steps: character classification and character localization. Firstly scale invariant
feature transform features are extracted and classified using support vector machines.
On this basis interest points are clustered according to their spatial information. Then,
characters are localized and eventually recognized by a weighted voting scheme of
pre-classified local descriptors. Preliminary results show that the proposed system can
handle highly degraded manuscript images with background noise, e.g. stains, tears,
and faded characters
Feature Mixing for Writer Retrieval and Identification on Papyri Fragments
This paper proposes a deep-learning-based approach to writer retrieval and
identification for papyri, with a focus on identifying fragments associated
with a specific writer and those corresponding to the same image. We present a
novel neural network architecture that combines a residual backbone with a
feature mixing stage to improve retrieval performance, and the final descriptor
is derived from a projection layer. The methodology is evaluated on two
benchmarks: PapyRow, where we achieve a mAP of 26.6 % and 24.9 % on writer and
page retrieval, and HisFragIR20, showing state-of-the-art performance (44.0 %
and 29.3 % mAP). Furthermore, our network has an accuracy of 28.7 % for writer
identification. Additionally, we conduct experiments on the influence of two
binarization techniques on fragments and show that binarizing does not enhance
performance. Our code and models are available to the community.Comment: accepted for HIP@ICDAR202
3D Acquisition of Archaeological Ceramics and Web-Based 3D Data Storage
Motivated by the requirements of modern archaeology, we are developing an automated system for archaeological classification and reconstruction of ceramics. The goal is to create a tool that satisfies the criteria of accuracy, performance (findings/hour), robustness, transportability, overall costs, and careful handling of the findings. Following our previous work, we present new achievements on the documentation steps for 3D acquisition, 3D data processing, and 3D reconstruction. We have improved our system so that it can handle large quantities of ceramic fragments efficiently and computes a more robust orientation of a fragment. In order to store the sherd data acquired and hold all the information necessary to reconstruct a complete vessel, a database for archaeological fragments was developed. We will demonstrate practical experiments and results undertaken onsite at different excavations in Israel and Peru
ECSIC: Epipolar Cross Attention for Stereo Image Compression
In this paper, we present ECSIC, a novel learned method for stereo image
compression. Our proposed method compresses the left and right images in a
joint manner by exploiting the mutual information between the images of the
stereo image pair using a novel stereo cross attention (SCA) module and two
stereo context modules. The SCA module performs cross-attention restricted to
the corresponding epipolar lines of the two images and processes them in
parallel. The stereo context modules improve the entropy estimation of the
second encoded image by using the first image as a context. We conduct an
extensive ablation study demonstrating the effectiveness of the proposed
modules and a comprehensive quantitative and qualitative comparison with
existing methods. ECSIC achieves state-of-the-art performance among stereo
image compression models on the two popular stereo image datasets Cityscapes
and InStereo2k while allowing for fast encoding and decoding, making it highly
practical for real-time applications
Readability Enhancement and Palimpsest Decipherment of Historical Manuscripts
This paper presents image acquisition and readability enhancement techniques for historical manuscripts developed in the interdisciplinary project “The Enigma of the Sinaitic Glagolitic Tradition” (Sinai II Project).1 We are mainly dealing with parchment documents originating from the 10th to the 12th centuries from St. Cather- ine’s Monastery on Mount Sinai. Their contents are being analyzed, fully or partly transcribed and edited in the course of the project. For comparison also other mss. are taken into consideration. The main challenge derives from the fact that some of the manuscripts are in a bad condition due to various damages, e.g. mold, washed out or faded text, etc. or contain palimpsest (=overwritten) parts. Therefore, the manuscripts investigated are imaged with a portable multispectral imaging system. This non-invasive conservation technique has proven extremely useful for the exami- nation and reconstruction of vanished text areas and erased or washed o palimpsest texts. Compared to regular white light, the illumination with speci c wavelengths highlights particular details of the documents, i.e. the writing and writing material, ruling, and underwritten text. In order to further enhance the contrast of the de- graded writings, several Blind Source Separation techniques are applied onto the multispectral images, including Principal Component Analysis (PCA), Independent Component Analysis (ICA) and others. Furthermore, this paper reports on other latest developments in the Sinai II Project, i.e. Document Image Dewarping, Automatic Layout Analysis, the recent result of another project related to our work: the image processing tool Paleo Toolbar, and the launch of the series Glagolitica Sinaitica
Transforming scholarship in the archives through handwritten text recognition:Transkribus as a case study
Purpose: An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. - Design/methodology/approach: This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. - Findings: Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. - Research limitations/implications: The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. - Practical implications: Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. - Social implications: The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. - Originality/value: This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector
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