Lamina caerulea ad Riding House Street Londinii locum ubi Equiano vixit et narrationem protulit monstrat.
Olaudah Equiano (c. 1745—31 Martii 1797[1]), vivus Gustavus Vassa appellatus,[2] fuit prominens Africanus Londiniensis, qui explorator, scriptor, mercator, libertus, et abolitionista motui Britannico servitutis abolendae in Britanniarum Regno favit. Eius autobiographia, The Interesting Narrative, anno 1789 edita, quae animos hominum ad se late convertit, magni momenti aestimabatur ob Actum Commercii Servorum 1807 perlatum, qui commercium servorum Africanorum in Britannia et eius coloniis prohibuit.
Index
1Nexus interni
2Notae
3Bibliographia
4Nexus externi
Nexus interni
Phillis Wheatley
Notae |
↑"Olaudah Equiano publisher=BBC History"
↑Lovejoy 2006.
Bibliographia |
Green, James. 1995. The Publishing History of Olaudah Equiano's Interesting Narrative. Slavery and Abolition 16(3):362–375.
Lovejoy, Paul E. 2006. Autobiography and Memory: Gustavus Vassa, alias Olaudah Equiano, the African. Slavery and Abolition 27(3):317–347.
Ogude, S. E. 1982. Facts into fiction: Equiano's narrative reconsidered. Research into African Literatures 13(1).
Ogude, S. E. 1984. Olaudah Equiano and the tradition of Defoe. African Literature Today 14.
Walvin, James. 1998. An African's Life: The Life and Times of Olaudah Equiano, 1745–1797. Londinii et Novi Eboraci: Cassell. ISBN 0304702145.
Nexus externi |
Carey, Brycchan. 2003–2005. Olaudah Equiano, or Gustavus Vassa, the African.
The Equiano Project. The Equiano Society et Birmingham Museum & Art Gallery.
Historic figures: Olaudah Equiano. BBC.
Olaudah Equiano. Africans in America, 1. Public Broadcasting Service.
Opera auctore "Olaudah Equiano" apud gutenberg.org reperta
Quinn, Frederick. 2002. Olaudah Equiano. Dictionary of African Christian Biography, ex African Saints: Saints, Martyrs, and Holy People from the Continent of Africa. Novi Eboraci: Crossroads Publishing Company.
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
