Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Abstract

Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.

Komparasi Metode Backpropagation Neural Network dan Convolutional Neural Network Pada Pengenalan Pola Tulisan Tangan

“…Dari proses digitalisasi, dapat ditarik kesimpulan dan analisa serta didapatkan informasi baru dari dokumen dan arsip-arsip tersebut [2]. Beberapa teknik analisa yang dapat diterapkan pada dokumen-dokumen ini adalah segmentasi halaman, spotting citra pada simbol dan angka, Optical Character Recognition (OCR) [3] dan Handwritten Text Recognition (HTR) [4].…”

Automatic Detection of Road Types From the Third Military Mapping Survey of Austria-Hungary Historical Map Series With Deep Convolutional Neural Networks

“…We used the open-source dhSegment toolbox [23] as in our previous segmentation studies [24] and [25]. The authors describe the toolbox as a general and flexible architecture for pixel-wise segmentation related tasks on historical documents.…”

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

“…Training a deep learning model with these modern datasets and testing with historical digits did not yield high accuracies in the literature [29]. Therefore, we created a dataset containing over 6000 digits, which contributes to the literature on this aspect [9]. The dataset can be accessed at https://urbanoccupations.ku.edu.tr/historicalarabic-handwritten-digit-dataset/ (accessed on 13 September 2021).…”

“…For this study, we focused on one location: Manisa town in western Anatolia in Turkey. We first employed a CNN-based page segmentation technique to retrieve demographic data of individuals by using the models developed in our previous studies [9,10]. After that, we used horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers and obtained the age data in the last line.…”