Character Recognition Of Seventeenth-Century Spanish American Notary Records Using Deep Learning

Handwritten character recognition is a challenging pattern recognition problem due to the inconsistency of the handwritten scripts and the lack of accurate labeled data. Historical documents written in cursive are even more challenging as characters have unique and varying shapes. Frequently, words are linked by lines and ornamental doodles. When historical documents are digitized, the images contain various types of noise and degradation, which further complicates the recognition of characters. In this paper, we present an empirical study of how well state-of-the-art convolutional neural networks (CNNs) for image classification perform for the task of recognizing handwritten characters in seventeenth-century Spanish American notarial scripts. Professional historians, paleography experts and trained labelers were involved in preparing the labeled dataset of Spanish characters for training the CNNs. The labeled dataset used in this experiment was created from the manuscripts written by one of the multiple scribes that contributed to the collection of approximately 220,000 digitized images of notary records housed at the Archivo General de la Nación Argentina (National Archives). We removed the noise in these images by applying standard image processing techniques. After training different CNNs, we computed the classification accuracy for all the characters. We observed that ResNet-50 achieved a promising accuracy of 97.08% compared to InceptionResnet-V2, Inception-V3, and VGG-16, which achieved 96.66%, 96.33% and 70.91%, respectively

Character recognition · Convolutional neural network · Cursive · Deep learning · Optical character recognition · Scripting language · Handwritten Text Recognition Techniques · Image and Object Detection Techniques · Image Processing and 3D Reconstruction