FP-THD: Full page transcription of historical documents
Aragon Institute of Engineering Research (I3A),Universidad de Zaragoza（阿拉贡工程研究院（I3A），萨拉戈塔大学）；Institute of Heritage and Humanities (IPH),Universidad de Zaragoza（遗产与人文研究院（IPH），萨拉戈塔大学）
H Neji, J Nogueras-Iso, J Lacasta, MÁ Latre, FJ García-Marco
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Figure 1: FP-THD architecture Overview: Layout Analysis and Masked Auto-encoder with Vision Trans- former
The transcription of historical documents written in Latin in XV and XVI centuries has special challenges as it must maintain the characters and special symbols that have distinct meanings to ensure that historical texts retain their original style and significance. This work proposes a pipeline for the transcription of historical documents preserving these special features. We propose to extend an existing text line recognition method with a layout analysis model. We analyze historical text images using a layout analysis model to extract text lines, which are then processed by an OCR model to generate a fully digitized page. We showed that our pipeline facilitates the processing of the page and produces an efficient result. We evaluated our approach on multiple datasets and demonstrate that the masked autoencoder effectively processes different types of text, including handwritten, printed and multi-language.
Extraction of text regions and individual text lines from historic documents is necessary for automatic transcription. We propose extending a CNN-based text baseline detection system by adding line height and text block boundary predictions to the model output, allowing the system to extract more comprehensive layout information. We also show that pixel-wise text orientation prediction can be used for processing documents with multiple text orientations. We demonstrate that the proposed method performs well on the cBAD baseline detection dataset. Additionally, we benchmark the method on newly …
End-to-End Page-Level Assessment of Handwritten Text Recognition
PRHLT Research Center, Universitat Politècnica de València（瓦伦西亚理工大学PRHLT研究中心）；Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI)（瓦伦西亚人工智能研究生院与研究网络（valgrAI））；U.I. for Computer Research, University of Alicante（阿利坎特大学计算机研究单元）
Classifying pages or text lines into font categories aids transcription because single font Optical Character Recognition (OCR) is generally more accurate than omni-font OCR. We present a simple framework based on Convolutional Neural Networks (CNNs), where a CNN is trained to classify small patches of text into predefined font classes. To classify page or line images, we average the CNN predictions over densely extracted patches. We show that this method achieves state-of-the-art performance on a challenging dataset of 40 Arabic computer fonts with 98.8\% line level accuracy. This same method…
Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；omni:us
In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simulta…
Recognizing the full-page of Japanese historical documents is a challenging problem due to the complex layout/background and difficulty of writing styles, such as cursive and connected characters. Most of the previous methods divided the recognition process into character segmentation and recognition. However, those methods provide only character bounding boxes and classes without text transcription. In this paper, we enlarge our previous humaninspired recognition system from multiple lines to the full-page of Kuzushiji documents. The human-inspired recognition system simulates human eye movem…
Identifying the type of font (e.g., Roman, Blackletter) used in historical documents can help optical character recognition (OCR) systems produce more accurate text transcriptions. Towards this end, we present an active-learning strategy that can significantly reduce the number of labeled samples needed to train a font classifier. Our approach extracts image-based features that exploit geometric differences between fonts at the word level, and combines them into a bag-of-word representation for each page in a document. We evaluate six sampling strategies based on uncertainty, dissimilarity and…
Full-Page Optical Music Recognition of Handwritten Monophonic Scores
Pattern Recognition and Artificial Intelligence group, University of Alicante（阿利坎特大学模式识别与人工智能组）；Instituto Superior de Enseñanzas Artísticas de la Comunidad Valenciana（瓦伦西亚自治区高等艺术教育机构）
Roma Tre University（罗马第三大学）；Vatican Secret Archives（梵蒂冈秘密档案馆）
In Codice Ratio is a research project to study tools and techniques for analyzing the contents of historical documents conserved in the Vatican Secret Archives (VSA). In this paper, we present our efforts to develop a system to support the transcription of medieval manuscripts. The goal is to provide paleographers with a tool to reduce their efforts in transcribing large volumes, as those stored in the VSA, producing good transcriptions for significant portions of the manuscripts. We propose an original approach based on character segmentation. Our solution is able to deal with the dirty segme…
Leveraging Morphology for Historical Script Metrological Analysis
LIGM, École des Ponts et Chaussées, IP Paris, CNRS, France（LIGM，国立桥路学校，巴黎理工学院，法国国家科学研究中心，法国）；Institut de Recherche et d’Histoire des Textes, Paris, Île-de-France, France（文本研究与历史研究所，巴黎，法兰西岛，法国）
ExpertHTR: Unified Handwritten Text Recognition with Multi-Task Learning and Sparse Mixture-of-Experts
University of Information Technology, Ho Chi Minh City, Vietnam（胡志明市信息技术大学）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国立大学胡志明市分校）；AJ Technologies, Nagoya, Japan（AJ科技公司）；RIKEN Center for Advanced Intelligence Project, Tokyo, Japan（理化学研究所先进智能研究中心）
A machine-readable catalogue of the Tsiolkovsky papers (fond 555, Archive of the Russian Academy of Sciences), and a way to measure how well its handwriting can be read
Durham University（杜伦大学）；Newcastle University（纽卡斯尔大学）；Southern University of Science and Technology（南方科技大学）
Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be presented with a large volume of scanned documents which need to be processed in a finite time. However, this problem is exacerbated both by the volume, in terms of scanned documents and the complexity of the pages, which need to be processed. Often containing many different elements, which each need to be processed and understood. Text recognition, which is a primary task of this process, is usually dependent upon the t…
