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I would like to make a semi-automatic OCR software for offline handwritten documents, where the OCR tries to recognize the words and the user has the ability to correct the fails of the recognizer by defining the misrecognized characters.

I found a similar issue but doesn't really answered my question: http://stackoverflow.com/questions/13117761/encog-neuroph-save-neural-network

My first approach is the following: I train a neural net before everything and run the first recognition on the selected document, then when the user define some specific character (for example the user selects one character on the image and says it's an "a" letter) it just modifies the weights in the neural net(runtime) and restarts the recognition on the document. Is it possible to "quick-train" the whole net only with some new spec characters so that the original characters could be still recognizable?

My second approach: I create two independent neural nets. The first is the universal as above and the second is an empty. After "universal" net finished the recognition I just add the new specified characters to train the second empty neural net and then restart the recognition with only this NN.

I would like to make a semi-automatic OCR software for offline handwritten documents, where the OCR tries to recognize the words and the user has the ability to correct the fails of the recognizer by defining the misrecognized characters.

I found a similar issue but doesn't really answered my question: https://stackoverflow.com/questions/13117761/encog-neuroph-save-neural-network

My first approach is the following: I train a neural net before everything and run the first recognition on the selected document, then when the user define some specific character (for example the user selects one character on the image and says it's an "a" letter) it just modifies the weights in the neural net(runtime) and restarts the recognition on the document. Is it possible to "quick-train" the whole net only with some new spec characters so that the original characters could be still recognizable?

My second approach: I create two independent neural nets. The first is the universal as above and the second is an empty. After "universal" net finished the recognition I just add the new specified characters to train the second empty neural net and then restart the recognition with only this NN.

I would like to make a semi-automatic OCR software for offline handwritten documents, where the OCR tries to recognize the words and the user have the ability to correlate the fails of the recognized by defining the misrecognized characters.

I found a similar issue but doesn't really answered my question: http://stackoverflow.com/questions/13117761/encog-neuroph-save-neural-network

My first approach is the following: I train a neural net before everything and run the first recognition on the selected document, then when the user define some specific character (for example the user selects one character on the image and says it's an "a" letter) it just modifies the weights in the neural net(runtime) and restarts the recognition on the document. Is it possible to "quick-train" the whole net only with some new spec characters so that the original characters could be still recognizable?

My second approach: I create two independent neural nets. The first is the universal as above and the second is an empty. After "universal" net finished the recognition I just add the new specified characters to train the second empty neural net and then restart the recognition with only this NN.

I would like to make a semi-automatic OCR software for offline handwritten documents, where the OCR tries to recognize the words and the user has the ability to correct the fails of the recognizer by defining the misrecognized characters.

I found a similar issue but doesn't really answered my question: http://stackoverflow.com/questions/13117761/encog-neuroph-save-neural-network

My first approach is the following: I train a neural net before everything and run the first recognition on the selected document, then when the user define some specific character (for example the user selects one character on the image and says it's an "a" letter) it just modifies the weights in the neural net(runtime) and restarts the recognition on the document. Is it possible to "quick-train" the whole net only with some new spec characters so that the original characters could be still recognizable?

My second approach: I create two independent neural nets. The first is the universal as above and the second is an empty. After "universal" net finished the recognition I just add the new specified characters to train the second empty neural net and then restart the recognition with only this NN.

I would like to make a semi-automatic ocr software for offline handwritten documents, where the ocr tries to recognise the words and the user has the ability to corrigate the fails of the recogniser by defining the misrecognised characters.

I found a similar issue, but doesn't really answered my question: http://stackoverflow.com/questions/13117761/encog-neuroph-save-neural-network

My first approach is the folowing: I train a neural net before everything and run a first recognition on the selected document, then when the user define some specific character (for example the user selects one character on the image and says its an "a" letter) it just modifies the weights in the neural net(runtime) and restarts the recognition on the document. Is it possible to "quick-train" the whole net only with some new spec characters so that the original characters could be still recognisable?

My second approach: I create two independent neural nets. The first is the universal as above and the second is an empty. After "universal" net finished the recognition I just add the new specified characters to train the second empty neural net and then restart the recognition with only this NN.

I would like to make a semi-automatic OCR software for offline handwritten documents, where the OCR tries to recognize the words and the user have the ability to correlate the fails of the recognized by defining the misrecognized characters.

I found a similar issue but doesn't really answered my question: http://stackoverflow.com/questions/13117761/encog-neuroph-save-neural-network

My first approach is the following: I train a neural net before everything and run the first recognition on the selected document, then when the user define some specific character (for example the user selects one character on the image and says it's an "a" letter) it just modifies the weights in the neural net(runtime) and restarts the recognition on the document. Is it possible to "quick-train" the whole net only with some new spec characters so that the original characters could be still recognizable?

My second approach: I create two independent neural nets. The first is the universal as above and the second is an empty. After "universal" net finished the recognition I just add the new specified characters to train the second empty neural net and then restart the recognition with only this NN.