Castra Vetera (vel etiam Vetera Castra aut breviter Vetera) erant castrum Romanum in provincia Germania inferiori apud Coloniam Ulpiam Traianam (breviter CUT sive CVT) et in agro praesentis urbis Xanti ad ripam fluminis Rheni.
Index
1Fontes
2Nexus interni
3Notae
4Nexus externi
5Literatur
Fontes |
Nomen Vetera a Cornelio Tacito citatur.[1]
Nexus interni
Limes Germanicus inferior
Notae |
↑Publius Cornelius Tacitus: Historiae 4, 21: Civilis adventu veteranarum cohortium iusti iam exercitus ductor, sed consilii ambiguus et vim Romanam reputans, cunctos qui aderant in verba Vespasiani adigit mittitque legatos ad duas legiones, quae priore acie pulsae in Vetera castra concesserant, ut idem sacramentum acciperent.Historien; 5, 14: At Civilis post malam in Treviris pugnam reparato per Germaniam exercitu apud Vetera castra consedit, tutus loco, et ut memoria prosperarum illic rerum augescerent barbarorum animi.
Nexus externi |
De castris veteris apud livius.org
Literatur |
Norbert Hanel: Die Militärlager von Vetera I und ihre Lagersiedlungen. In: Martin Müller, Hans-Joachim Schalles, Norbert Zieling: Colonia Ulpia Traiana. Xanten und sein Umland in römischer Zeit. Mainz 2008. ISBN 978-3-8053-3953-7, p. 93–107.
Julia Obladen-Kauder: Spurensuche in Xanten. Ein archäologischer Wanderführer. Köln 2005. ISBN 3-88094-927-1.
Werner Böcking: Die Römer am Niederrhein. Geschichte und Ausgrabungen. Essen. 2005. ISBN 3-89861-427-1.
Norbert Hanel: Vetera I. Die Funde aus den römischen Lagern auf dem Fürstenberg bei Xanten. Köln 1995; Habelt, Bonn 1995. ISBN 3-7927-1248-2.
Norbert Hanel: Zum antiken Namen der Legionslager auf dem Fürstenberg bei Xanten: Vetera castra. In: Xantener Berichte. Sammelband 5, Rheinland Verlag, Köln 1994. ISBN 3-7927-1415-9, p. 263–265.
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...
