DHQ: Digital Humanities Quarterly
2021
Volume 15 Number 4
Volume 15 Number 4
Character Recognition Of Seventeenth-Century Spanish American Notary Records Using Deep Learning
Abstract
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 Historical documents written in cursive are even more challenging as characters have unique and varying shapes.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 When historical documents are digitized, the images contain various types of noise and degradation, which further complicates the recognition of characters.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.
I. INTRODUCTION [1]
Notary records contain a wealth of information for understanding different aspects of
the human experience. For that reason, historians specialized in different regions
and time periods employ them in writing social, economic, political, and cultural
histories. The seventeenth-century Spanish American notarial scripts housed in the
National Archives of Argentina are among the most challenging collections, as they
were written by multiple hands, for an audience of experts, and at a time the
written Spanish language underwent significant transformations [Silva Prada 2001] [Wasserman 2018]
.[2] Consequently, it takes years of training
and practice in seventeenth-century Spanish paleography to become proficient in
reading and analyzing these notarial scripts (Fig.1). On an average, it takes expert
Spanish speaking readers about one hour to read a four to five pages long notarized
deed. The task is even more daunting for non-native Spanish speakers.
Figure 1.
Samples of different handwriting styles present in the collection of
seventeenth-century notarial scripts used for this studyDigitization significantly aided in the preservation of these records and made them
relatively more accessible primarily by enabling their duplication without damaging
the originals. However, despite the quantity and variety of documents this
collection compiles, these records are still waiting to be fully utilized in
scholarly endeavors. To this day, researchers and students rely on traditional, time
consuming, and expensive methods of archival research to access these documents and
archival discovery primarily depends on the skill, patience and luck of the scholar.
The development of a system capable of storing, reading, querying, and analyzing
this historical collection is crucial as it will make these manuscripts accessible
to a broader community of researchers without requiring extensive and expensive
paleography training. Character recognition is the first step in the development of
such a system.
Today, using optical character recognition (OCR), we can automatically convert
printed or handwritten text into machine-readable, editable, and searchable text.
For that reason, OCR is regarded as the heart of many document analysis systems.
Unlike the recognition of printed text, historical handwritten text presents unique
challenges. Written in cursive, historical scripts usually employ irregular
characters and capitalization, abbreviations, archaic spelling, and linked words
(Fig. 2).
[3] Additionally, the scans contain different types of noise
including discoloration, stains, as well as ink bleeds and smudges. In order to
enable OCR tasks, researchers applied different methods including Support Vector
Machine (SVM) [Vellingiriraj et al. 2016], K-NN [Chammas et al. 2018], and deep
learning [Granell et al. 2018].
Figure 2.
Example of some of the challenges
present in the manuscripts utilized for this study. On average, one
page has 24 lines. The variations showed in this short paragraph are
standard throughout the deeds.The challenges of converting Spanish American historical texts into machine-readable
text have been pointed out by Hannah Alpert-Abrams. She used Ocular, the OCR tool developed by
Taylor Berg-Kirkpatrick, to machine read the Primeros Libros,
a sixteenth-century, printed, bilingual (Spanish and Nahuatl) text [Alpert-Abrams 2016]. Her experiments
delivered not only dirty OCR but also linguistic errors that could lead to
inaccuracies in cultural translation. We experimented with Transkribus, another popular
tool, which also yielded an inaccurate output for our collection of
seventeenth-century, handwritten, Spanish American notarial records. However, in
recent years, deep learning has achieved remarkable success for image understanding
and classification, image segmentation, speech recognition, and natural language
processing. In this work, we explore if deep learning, and more specifically CNNs,
can enable accurate recognition of characters in Spanish American notarial scripts.
Deep learning techniques perform better and achieve higher accuracy when large
labeled datasets are available (e.g., ImageNet, MS COCO) [Deng et al. 2009] [Lin et al. 2014].
However, there is no readily available labeled dataset for
seventeenth-century historical texts. Although data labeling is an expensive,
error-prone, and time-consuming task, with the help of professional historians and
paleography experts, we manually prepared a labeled dataset. Additionally,
historical texts have various kinds of noises and degradation and the uneven
scanning quality of the images pose additional challenges for image preprocessing
and cleaning as it has to be performed without losing any of the essential features
that define each of the characters.
In this paper, we present an empirical study of how well state-of-the-art CNNs
perform for the task of recognizing handwritten characters in seventeenth-century
Spanish American notarial scripts. To the best of our knowledge, this is the first
effort towards automatically recognizing characters in seventeenth-century Spanish
American notarial scripts.
The key contributions of our work are the following:
- With the assistance of professional historians as well as labelers proficient in Spanish and trained in paleography, we prepared the training dataset in two steps. Firstly, we collected from our labelers 250 unique samples of each of the characters present on the manuscripts. Secondly, we augmented the dataset and generated additional characters by applying random distortions and rotations to the original ones. For quality control, before training CNNs on the generated dataset, our professional historians verified that the expanded characters resembled the original ones. There are certain characters that are rare in both, the Spanish language and the notarial scripts. For those characters we had fewer labels.
- We selected four state-of-the-art CNNs, namely, Inception-v3 [Szegedy et al. 2015] [Szegedy et al. 2016], ResNet-50 [He et al. 2016], VGG-16 [Simonyan and Zisserman 2014] and InceptionResNet-v2 [Szegedy et al. 2017] to recognize high frequency characters as well as rare characters (i.e., x and z). We trained these networks by configuring the hyperparameters to achieve the best classification accuracy. Our experiments showed that ResNet-50 achieved the best classification accuracy of 97.08% whereas other networks achieved lower accuracy with VGG-16 being the poorest.
- For broader use by the academic community and to foster new research in transcribing historical texts, our labeled dataset and software are publicly available on GitHub via https://github.com/UMKC-BigDataLab/DeepLearningSpanishAmerican.
This paper is organized as follows. Section II discusses the relevant related works
and the motivation for this study. Section III presents the methodology we employed
and discusses our evaluation results. Finally, we conclude in Section IV and outline
our future work.
II. RELATED WORK & MOTIVATION
Deep learning approaches have been widely used for handwritten text recognition of
many modern languages. CNNs
[Krizhevsky et al. 2012] are
among the most popular deep learning methods and have a proven record of outstanding
performance when applied to image recognition tasks. Additionally, CNNs have shown
an outstanding success when applied to the MNIST dataset [LeCun et al. 1998].
Ashiquzzaman et al. proposed a CNN-based model using ReLU activation function and
dropout as a regularization layer that has achieved 97.4% accuracy [Ashiquzzaman and Tushar 2017].
Tsai investigated various convolutional neural network architectures
for handwritten Japanese character recognition and created a model with a 96.1%
recognition rate for character classification [Tsai 2016]. Another CNN-based model
has been proposed by Rabby et al. to classify Bangla handwriting characters. A
95.71% validation accuracy was achieved for the BanglaLekha-Isolated dataset [Rabby et al. 2018].
However, applying these methods to historical documents present unique challenges due
to the quality of the scanned images, writing style variations, and the lack of
labeled data. Consequently, only a few studies have taken this path. For instance,
Kölsch et al. used a Fully Convolutional Neural Network (FCNN)-based approach for
historic German documents, which achieved 95.6% accuracy [Kolsch et al. 2018].
Clanuwat et al. proposed a KuroNet model that jointly recognizes an entire page of
text by using a residual U-Net architecture and predicts the location and identity
of all characters on a given page. Additionally, their proposed system was able to
successfully recognize a significant fraction of pre-modern Japanese documents
[Clanuwat].
Researchers tend to combine CNNs with recurrent neural networks (RNNs) to further
improve accuracy. That was the case for Granell et al. who proposed a handwritten
text recognition system to transcribe a corpus of Spanish medieval scrips based on a
CNN and RNN [Granell et al. 2018]. The authors showed that deep learning
approaches outperform the traditional machine learning models such as Hidden Markov
Model-based systems. Dona Valy et al. evaluated different deep learning approaches
for character recognition that have been constructed from Khmer palm leaf
manuscripts [Valy et al. 2018]. The authors showed that the combination of CNN and
RNN-based architectures achieves better results with a 5.01% error rate. Finally,
Chammas et al. presented a CRNN system for text- line recognition of historical
documents [Chammas et al. 2018]. They showed how to train the system with only 10%
manually labeled text-line data from the READ 2017 dataset.
Next, we describe the salient features of four recent CNNs that we used in our
study.
- VGGIn 2014, Karen Simonyan and Andrew Zisserman (2014) proposed the VGGNet for the Large Scale Visual Recognition Challenge (ILSVRC2014). The key contribution from this model was to increase the depth of the architecture by using a 3x3 convolutional filters to achieve higher performance. The VGG model achieved 92.7% top-5 accuracy on the ImageNet dataset and won the ILSVRC2014 challenge. For our experimental study, we chose VGG-16 as a representative of the VGGNet due to its smaller number of parameters compared to VGG19.
- InceptionInception architecture was first proposed in 2014 by Szegedy et al. The authors claimed that deeper networks are more prone to overfitting and consume computational resources. They solved that challenge by moving from fully connected to sparsely connected architectures. They introduced the inception layer, which is a combination of three different convolutional layers (1x1 convolutional layer, 3x3 convolutional layer, and 5x5 convolutional layer) with a max pooling layer that operates at the same level. Their outputs are concatenated to be the input of the next layer. This architecture has been updated to increase the accuracy further and proved that any convolution with kernel size more substantial than 3x3 could be represented efficiently with a series of smaller convolutions. In our experimental study, we used Inception- V3 [Szegedy et al. 2015] [Szegedy et al. 2017].
- ResNetHe et al. introduced the deep residual neural network (ResNet) architecture and won the first place in the ILSVRC 2015 classification competition. ResNet introduces the idea of identity connections that skip one or more layers to train deeper neural networks. This resolved the vanishing gradient problem by allowing the gradients to flow directly through the skipped connections backward from later layers to the initial filter. For our experimental study, we used ResNet-50 as a representative [He et al. 2016].
- InceptionResNetInception-ResNet is a convolutional neural network proposed by Szegedy et al. in 2016. It was trained on more than one million images from the ImageNet database and achieved a 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge [Szegedy et al. 2017] The success of residual connections in training very deep architectures and the performance of the Inception-V3 inspired the authors to replace the Inception filter concatenation step with residual connections. This combination allows Inception to obtain all the advantages of the residual approach but with the preservation of its computational efficiency. We used InceptionResNet-v2 as a representative for our experimental study.
Despite these advances, to this day, there is a lack of end-to-end systems capable of
managing and analyzing historical documents in general and those in Spanish in
particular. This gap, coupled with the professional training needs of 21st century humanities scholars, draw our attention and
drives our experimentation efforts to make these manuscripts accessible to a broader
community of researchers without requiring extensive and expensive paleography
training. Our effort is the first step in this direction and will open up a wide
range of research opportunities for others in the academic community.
III. METHODOLOGY
In this section, we present the methodology for conducting this empirical study. The
overall steps are illustrated in Figure 3. There are four main stages: (a)
pre-processing, (b) dataset preparation, (c) training and validation (to tune the
hyperparameters), and (d) testing the accuracy of character recognition.
Figure 3.
Overall
steps involved in character recognition of seventeenth-century
Spanish American notarial scriptsa) Pre-processing:
The manuscripts scans contained noise including spurious ink
markings, ink smudges and bleeds. Such noise affects the feature extraction as well
as the classification. Before constructing the character dataset, we reduced the
noise on the manuscripts images (Figure 4). The following preprocessing techniques
allowed us to clean the images without affecting the quality of the written content.
Firstly, we converted the original manuscript images into grayscale. Secondly, we
applied a median filter to soften the images backgrounds and remove background
noise. Finally, we applied image binarization to convert the grayscale images into
black and white ones as this technique significantly reduces the information
contained in an image and increases the training speed [Chamchong and Fung 2009].
Figure 4.
An example of an original scan and its cleaned version.b) Dataset preparation:
We constructed the character dataset from clean images. Colabeler tool was used to annotate and
label the characters. The annotations were exported in JSON format along with each
character label and its corresponding coordinates. We ran a Python script to crop
and save every character in .png format. As it was difficult to keep each character
within square dimensions while annotating them, each one of them was padded with
white pixels and resized to fixed dimensions for training purposes. We considered 24
characters that comprise most of the Spanish alphabet: a, b, c, c, d, e, f, g, h, i, j, l, m, n, o, p,
q, r, s, t, u/v, x, y, and z. Note
that the "u" and the "v" were interchangeable in
seventeenth-century Spanish, and thus, the alphabets provided by most paleography
manuals list them as a single character. We followed this standard and treated them
as single letter (u/v). Additionally,
characters such as "k" and "w" are infrequently used in modern Spanish
and were so uncommon in seventeenth-century notarial scripts that paleography
textbooks do not even list them on their sample alphabets.[4]
To build our sample dataset, we selected the hand of Nicolas de Valdibia y Brizuela
who, by 1650, acted as an interim notary in Buenos Aires, Argentina. As opposed to
those who held permanent positions, interim notaries did not receive extensive
training nor were they skilled scribes. Thus, the experiments we report in this
paper were based on very irregular and, therefore, hard to read scripts, as shown in
Figure 2 & Figure 4.
Our labeling team labeled 250 unique samples for every character. This resulted in a
total of 6,000 original images. As deep learning models perform well on large
labeled datasets, the dataset was augmented by applying random distortion and
rotation of +5 and -5 degrees without affecting the shape and/or the direction of
each character. A few examples are shown in Figure 5.
Figure 5.
Example of the original characters “b”; “d” and “p”
b) Example of
augmented characters after applying random distortion and
rotationFor quality control, before training CNNs on the generated dataset, the professional
historians and paleography experts in our team verified that the expanded characters
resembled the original ones. Our dataset contained 1,000 samples for each character
out of which 250 samples were manually labeled, and 750 samples were generated. This
resulted in a total of 24,000 images.
c) Training and validation:
We conducted all our experiments on a GeForce RTX 2080 Ti
GPU with 12GB GPU memory. Our software was implemented using Keras [Chollet et al. 2015] with TensorFlow backend
[Abadi et al. 2015], TensorBoard,[5] and
OpenCV [Bradski 2000].
We trained the state-of-art CNNs using 24,000 images (1,000 images per character) in
our labeled dataset prepared from our seventeenth-century Spanish American notary
scripts. For each character, we split the training set into an 80-20 split. The
samples not used for training were part of the testing set. The testing set
contained 50 samples per character and were preprocessed in the same way as the
training images. Data augmentation was applied to the training set to avoid
overfitting. As we mentioned earlier, most of the paleography manuals list the “u” and the “v” as a single character as they were
interchangeable in seventeenth-century Spanish. We followed the same standard and
treated them as a single letter (u/v).
d) Test of the accuracy of character recognition:
In this research, the character
recognition accuracy was used as the primary metric to evaluate the performance of
different CNNs. An empirical tuning approach has been followed to tune the
hyperparameters to obtain higher character recognition accuracy. To ensure a fair
comparison, we set a total of 150 epochs to train the models, and used the Adam
optimizer [Kingma and Ba 2015]. Adam is a gradient descent optimization algorithm
that is popularly used in training deep learning models. (Using gradient descent, it
is possible to find local minima of functions during optimization.) The performance
of the models with different hyperparameters values is shown in Table I and Table
II.
Table I shows the recognition accuracy when we set the dropout value to 0.6.
ResNet-50 achieved the highest accuracy of 97.02%, followed by InceptionResnet-v2,
Inception-v3, and VGG-16 with a recognition accuracy of 96.33%, 93.83%, and 96.33%,
respectively. The performance of most of the networks improved when the batch size
was increased to 64 except for Inception-v3, which achieved a better recognition
accuracy when the batch size was 32.
The recognition accuracy results obtained from using 0.5 dropout rate are presented
in Table II. The recognition accuracy decreased for most of the networks when we
changed the dropout rate from 0.6 to 0.5. VGG-16 was the only model where it
performed better on a 0.5 dropout rate.
| Models | Batch Size 32 | Batch Size 64 |
| VGG-16 | 62.50% | 69.33% |
| Inception-V3 | 94.91% | 93.83% |
| ResNet-50 | 95.50% | 97.08% |
| InceptionResnet-V2 | 96.00% | 96.33% |
Table 1.
Recognition accuracy obtained with 0.6 dropout rate; best accuracy is shown in blue
and the worst in red| Models | Batch Size 32 | Batch Size 64 |
| VGG-16 | 70.33% | 70.91% |
| Inception-V3 | 93.83% | 96.33% |
| ResNet-50 | 96.92% | 87.58% |
| InceptionResnet-V2 | 96.66% | 94.08% |
Table 2.
Recognition accuracy obtained with 0.5 dropout rate; best accuracy is shown in blue and the worst in redTable III shows the accuracy breakdown of each character obtained from two different
experiments. We labeled the best results in bold and worse results in italics. As shown
in the table, VGG-16 fails in recognizing non-confusing characters such as "o" and "u/v". However, it performs well on few characters such as “m” and “y”.
| Batch Size = 32 & Dropout rate = 0.5 | Batch Size = 64 & Dropout rate = 0.5 | |||||||
| VGG | Inception | ResNet | Inception | VGG | Inception | ResNet | Inception | |
| 16 | v3 | 50 | Resnet v2 | 16 | v3 | 50 | Resnet v2 | |
| A | 82% | 94% | 94% | 96% | 92% | 94% | 90% | 92% |
| B | 82% | 100% | 100% | 96% | 86% | 98% | 98% | 92% |
| C | 62% | 92% | 94% | 94% | 42% | 94% | 86% | 92% |
| c¸ | 76% | 100% | 98% | 100% | 74% | 100% | 98% | 98% |
| D | 74% | 92% | 96% | 96% | 74% | 94% | 76% | 96% |
| E | 36% | 84% | 90% | 84% | 46% | 90% | 76% | 86% |
| F | 38% | 86% | 88% | 92% | 60% | 92% | 92% | 94% |
| G | 22% | 94% | 96% | 98% | 32% | 88% | 68% | 68% |
| H | 74% | 98% | 100% | 98% | 68% | 100% | 78% | 100% |
| I | 64% | 100% | 96% | 100% | 64% | 96% | 44% | 100% |
| J | 88% | 100% | 98% | 96% | 86% | 100% | 96% | 100% |
| L | 76% | 100% | 100% | 100% | 70% | 100% | 80% | 100% |
| M | 98% | 100% | 100% | 96% | 96% | 100% | 98% | 100% |
| N | 90% | 94% | 96% | 96% | 76% | 92% | 56% | 78% |
| O | 82% | 100% | 100% | 100% | 82% | 100% | 100% | 100% |
| P | 88% | 98% | 98% | 100% | 94% | 98% | 98% | 98% |
| Q | 74% | 100% | 100% | 100% | 78% | 100% | 100% | 98% |
| R | 58% | 92% | 96% | 98% | 44% | 94% | 96% | 90% |
| S | 74% | 94% | 94% | 92% | 58% | 96% | 94% | 90% |
| T | 64% | 96% | 100% | 100% | 82% | 96% | 94% | 96% |
| u/v | 68% | 100% | 100% | 100% | 64% | 100% | 100% | 100% |
| X | 52% | 62% | 92% | 90% | 66% | 90% | 84% | 90% |
| Y | 94% | 100% | 100% | 100% | 88% | 100% | 100% | 100% |
| Z | 72% | 100% | 100% | 98% | 80% | 100% | 100% | 100% |
Table 3.
Recognition accuracy per character. Best results are highlighted in bold and the worst results are highlighted in italicsOverall, VGG-16 performed the worst for most of our character datasets. Figure 6
gives the graphs of accuracy and loss values for the training set with respect to
the number of epochs. It shows that VGG-16 accuracy could have been improved if it
trained with more epochs.
Figure 6.
The accuracy (left) and loss (right) curves on the training set of the
CNN modelsTo further understand why some models achieved low accuracy, we generated confusion
matrices for all the models. Confusion matrices help us study the miss-classified
characters. As seen in Figure 7, confusion matrices confirm that most of the
confusions occur between the characters that are written similarly. For instance,
the character “n” is confused with “r” as shown in Figure 7(a), and “g” is confused with “q” as shown in Figure 7(b). The results
are not surprising as these characters generally confuse non-expert human readers
and, occasionally, trained paleographers. Figure 8 shows samples of these
characters. However, as shown on Table III, the recognition accuracy remains overall
strong.
Figure 7.
(a) ResNet-50 Confusion
Matrix (b) InceptionReset-v2 Confusion
Matrix
Confusion
matrices of selected models to show the miss-classified
characters
Figure 8.
a) Example of the shape similarities between the characters “r” and “n”.
b) Example of the shape similarities between the characters “g,” “q,” and “y”IV. CONCLUSION & FUTURE WORK
Historical handwritten character recognition is a challenging pattern recognition
problem due to the inconsistency of the handwritten scripts and the lack of accurate
labeled data. In this paper, we presented an empirical study on how state-of-
the-art CNNs (developed for image classification) perform for the task of
recognizing handwritten characters in seventeenth-century Spanish American notarial
scripts. The labeled dataset employed in this study was carefully curated with the
help of paleography experts and professional historians. Data augmentation was
employed to increase the number of training samples. We observed that ResNet-50
achieved the promising accuracy of 97.08% compared to InceptionResnet- V2,
Inception-V3, and VGG-16, which achieved 96.66%, 96.33%, and 70.91%, respectively.
Our study demonstrates that recent CNNs are promising to detect characters in
seventeenth-century Spanish notarial scripts. Our future work will test the
performance of deep learning-based OCR models such as Keras-OCR, YOLO-OCR, Tesseract
and Kraken for the detection and recognition of handwritten words on these
manuscripts. Accurate word recognition will be a necessary step in the development
of a tool for reading, querying, and analyzing this historical collection. We plan
model the content of these manuscripts in a form that would make information
retrieval faster and better.
The labeled dataset and software used in this study are publicly available on GitHub
via https://github.com/UMKC-BigDataLab/DeepLearningSpanishAmerican.
ACKNOWLEDGEMENTS
This research was supported by the NEH Digital Humanities Advancement Grant
(HAA-271747-20), UMKC’s Missouri Institute for Energy and Defense (MIDE), UMKC’s
Funding for Excellence Program, and a Collaborative Data Science Grant from UMKC’s
Institute for Data Education, Analytics and Science (IDEAS). The authors would like
thank Ryan Rowland, Maha Alrasheed, and Vania Todorova for labeling data as well as
the Archivo General de la República Argentina for granting
their permission to use in this study their digitized collection of notary records.
Dr. Martin L. E. Wasserman contributed to this project with his expertise in Spanish
paleography. The first author (N. A.) would like to thank UMKC’s Women’s Council
Graduate Assistance Fund (GAF) as well as the University of Tabuk in Saudi Arabia
for sponsoring her scholarship.
Notes
[1] Viviana Grieco’s research focuses on Colonial Latin
American history and has received extensive paleography training in
Argentina and in Spain. She leads our labeling team which counts on the
expertise of Martin Wasserman and David Freeman, historians who have
employed in their research the collection of notary records used in this
study. For more information about our research team visit https://www.umkc.edu/mide/NEH-Project/
[2] The speed and volume of the documentary production
forced the scribes to link words and use an increasing number of
abbreviations. The office of the public notary in seventeenth century
Buenos Aires had a high turnover rate, which explains the large number
of interim notaries as well as the variety of hands present in this
collection.
[3] The Document Analysis Group at the
Universitat Autònoma de Barcelona has been developing a digital library
for the sixteenth-century Llibres d’Esposalles
(marriage records). These handwritten marriage records are quite
challenging although each marriage license follows a regular formula and
the scripts are more consistent than those for the seventeenth-century
notary records, http://dag.cvc.uab.es/the-esposalles-database/
[4] N.
Silva-Prada, Manual de paleografía y diplomática hispanoamericana,
siglos XVI, XVII y XVIII. Libros de texto, manuales de prácticas y
antologías. Universidad Autónoma Metropolitana, Unidad Iztapalapa, 2001.
https://paleografi.hypotheses.org/el-manual-de-silva-prada
[5] Tensorflow,
“tensorflow/tensorboard,” Apr 2020. Available: https://github.com/tensorflow/tensorboard
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DHQ has been made possible in part by the National Endowment for the Humanities.
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Unless otherwise noted, the DHQ web site and all DHQ published content are published under a Creative Commons Attribution-NoDerivatives 4.0 International License. Individual articles may carry a more permissive license, as described in the footer for the individual article, and in the article’s metadata.
