End-to-end platform evaluation for Spanish Handwritten Text Recognition

The task of automated recognition of handwritten texts requires various phases and technologies both optical and language related. This article describes an approach for performing this task in a comprehensive manner, using machine learning throughout all phases of the process. In addition to the explanation of the employed methodology, it describes the process of building and evaluating a model of manuscript recognition for the Spanish language. The original contribution of this article is given by the training and evaluation of Offline HTR models for Spanish language manuscripts, as well as the evaluation of a platform to perform this task in a complete way. In addition, it details the work being carried out to achieve improvements in the models obtained, and to develop new models for different complex corpora that are more difficult for the HTR task.

Digitising (Romanian) Cyrillic using Transkribus: new perspectives

In this paper we discuss the application of the software platform Transkribus (transkribus.eu), an AI-assisted tool for Handwritten Text Recognition (HTR), to 16th century Romanian manuscript and printed sources using Cyrillic scripts. After an overview of the basic functionality of the HTR technology and Transkribus, we discuss the Romanian and bilingual Slavonic-Romanian sources we used, give an insight on training specific and generic as well as smart (i.e. transliterating from Cyrillic into Latin script) models, evaluate their performance and discuss implications of HTR for philological research in the Digital Age. We conclude with an outlook on future research perspectives.

HTR for Greek Historical Handwritten Documents

Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, the challenge in HTR is related to a focused goal of the transcription of Greek historical manuscripts that contain several particularities. To this end, in this paper, a convolutional recurrent neural network architecture is proposed that comprises octave convolution and recurrent units which use effective gated mechanisms. The proposed architecture has been evaluated on three newly created collections from Greek historical handwritten documents that will be made publicly available for research purposes as well as on standard datasets like IAM and RIMES. For evaluation we perform a concise study which shows that compared to state of the art architectures, the proposed one deals effectively with the challenging Greek historical manuscripts.

Handwritten Text Recognition Using Machine Learning

The human race has shown a huge interest in machines over the years and has developed and advanced to a very large extent in this domain. Starting from the object identification and classification through pictures to editing for the captured image or video everything can be performed through machines and advanced systems, one such part of this advanced technology is deep learning or machine learning. which comes with its own individual set of modules, algorithms, and techniques. Similar to this domain one such idea which was discovered is handwritten digit recognition. This is one of such areas where development and research occur around the numerical also known as digits, where a number of possibilities, permutations, and combinations are attained to gain accurate results this can also be perceived as the ability of computers to interpret and understand the given input which is through number plates, photographs, documents or can be in a handwritten format and to convert it in digital format as an output through screens.

Automatic Estimation of Age Distributions from the First Ottoman Empire Population Register Series by Using Deep Learning

Recently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; it directly affects the character segmentation stage, which in turn determines the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and the 1860s. Then, we employed horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers. We further trained a CNN model to recognize automatically detected ages of individuals and estimated age distributions of people from these historical documents. Extracting age information from these historical registers is significant because it has enormous potential to revolutionize historical demography of around 20 successor states of the Ottoman Empire or countries of today. We achieved approximately 60% digit accuracy for recognizing the numbers in these registers and estimated the age distribution with Root Mean Square Error 23.61.

Species Detection and Segmentation of Multi-specimen Historical Herbaria

Historically, herbarium specimens have provided users with documented occurrences of plants in specific locations over time. Herbarium collections have therefore been the basis of systematic botany for centuries (Younis et al. 2020). According to the latest summary report based on the data from Index Herbariorum, there are around 3400 active herbaria in the world containing 397 million specimens that are spread across 182 countries (Thiers 2021). Exponential growth in high quality image capturing devices induced by the enormous amount of uncovered collections has further led to rising interest in large scale digitization initiatives across the world (Le Bras et al. 2017). As herbarium specimens are increasingly becoming digitised and accessible in online repositories, an important need has also emerged to develop automated tools to process and enrich these collections to facilitate better access to the preserved archives. This rising number of digitised herbarium sheets provides an opportunity to employ computer-based image processing techniques, such as deep learning, to automatically identify species and higher taxa (Carranza-Rojas and Joly 2018, Carranza-Rojas et al. 2017, Younis et al. 2020) or to extract other useful information from the herbaria sheets, such as detecting handwritten text, color bars, scales and barcodes. The species identification task works well for herbarium sheets that have only one species in a page. However, there are many herbarium books that have multiple species on the same page (as shown in Fig. 1) for which the complexity of the identification problem increases tremendously. It also involves a great deal of time and effort if they are to be enriched manually. In this work, we propose a pipeline that can automatically detect, identify, and enrich plant species in multi-specimen herbaria. The core idea of the pipeline is to detect unique plant species and handwritten text around the plant species and map the text to the correct plant species. As shown in Fig. 2, the proposed pipeline begins with the pre-processing of the images. The images are rotated and aligned such that the longest edge is maintained as its height. In the case of herbarium books, the pages are detected and morphological transformations are performed to reduce occlusions (Thirukokaranam Chandrasekar and Verstockt 2020). A YOLOv3 (You Only Look Once version 3) object detection model (Zhao and Li 2020) is trained from scratch to detect plants and text. The model was trained on a dataset of single species herbarium sheets with a mosaic augmentation technique to extend the plants model to detect multiple species. The first results of the training shows impressive results although it could be further improved with more labelled data. We also plan to train an object segmentation model and contrast its performance with the plant detection model for multi-specimen herbarium sheets. After detecting both the plants and the text, the text will be recognized with a state-of-the-art handwritten text recognition (HTR) model. The recognized text can then be matched with a database of specimens, to identify each detected specimen. Furthermore, additional textual metadata (e.g. date, locality, collector's name, institution) visible on the sheet will be recognized and used to enrich the collection.

A compact deep learning model for Khmer handwritten text recognition

<p>The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.</p>