def ocr_box_file(self, box_fname):
# Set up the names of output files
replace = lambda s: box_fname.replace('.box', s)
asis_fname = replace('.ml.txt')
nogram_out_fname = replace('.nogram.txt')
ngram_out_fname = replace('.gram.txt')
log_fname = replace('.{}.log'.format(self.loglevelname))
logging.basicConfig(filename=log_fname,
level=self.loglevel,
filemode="w")
# Read Bantries & get Most likely output
bf = BantryFile(box_fname)
with open(asis_fname, 'w', encoding='utf-8') as f:
f.write(post_process(bf.text))
# Process using ngrams
ngrammed_lines, notgrammed_lines = [], []
for linenum in range(bf.num_lines):
print("Line ", linenum)
line_bantries = bf.get_line_bantires(linenum)
gramgraph = GramGraph(line_bantries)
gramgraph.process_tree()
notgrammed_lines.append(gramgraph.get_best_apriori_str())
ngrammed_lines.append(gramgraph.get_best_str())
nogram_out = post_process("\n".join(notgrammed_lines))
with open(nogram_out_fname, 'w', encoding='utf-8') as out_file:
out_file.write(nogram_out)
ngram_out = post_process("\n".join(ngrammed_lines))
with open(ngram_out_fname, 'w', encoding='utf-8') as out_file:
out_file.write(ngram_out)
print("Input : ", box_fname)
print("As is output : ", asis_fname)
print("Without ngram : ", nogram_out_fname)
print("With ngram : ", ngram_out_fname)
print("Log : ", log_fname)
def __init__(self,
nnet_fname,
scaler_fname,
labels_fname,
ngram_fname,
logbase=1,
loglevel=logging.INFO,):
self.nnet_fname = nnet_fname
self.scaler_fname = scaler_fname
self.labels_fname = labels_fname
self.ngram_fname = ngram_fname
self.logbase = logbase
self.loglevel = loglevel
self.loglevelname = logging._levelToName[loglevel].lower()
Bantry.scaler = ScalerFactory(scaler_fname)
Bantry.classifier = Classifier(nnet_fname, labels_fname,
logbase=logbase)
self.ng = Ngram(ngram_fname)
Bantry.ngram = self.ng
GramGraph.set_ngram(self.ng)
def __init__(
self,
nnet_fname,
scaler_fname,
labels_fname,
ngram_fname,
logbase=1,
loglevel=logging.INFO,
):
self.nnet_fname = nnet_fname
self.scaler_fname = scaler_fname
self.labels_fname = labels_fname
self.ngram_fname = ngram_fname
self.logbase = logbase
self.loglevel = loglevel
self.loglevelname = logging._levelToName[loglevel].lower()
Bantry.scaler = ScalerFactory(scaler_fname)
Bantry.classifier = Classifier(nnet_fname,
labels_fname,
logbase=logbase)
self.ng = Ngram(ngram_fname)
Bantry.ngram = self.ng
GramGraph.set_ngram(self.ng)
def ocr_box_file(self, box_fname):
# Set up the names of output files
replace = lambda s: box_fname.replace('.box', s)
asis_fname = replace('.ml.txt')
nogram_out_fname = replace('.nogram.txt')
ngram_out_fname = replace('.gram.txt')
log_fname = replace('.{}.log'.format(self.loglevelname))
logging.basicConfig(filename=log_fname,
level=self.loglevel,
filemode="w")
# Read Bantries & get Most likely output
bf = BantryFile(box_fname)
with open(asis_fname, 'w', encoding='utf-8') as f:
f.write(post_process(bf.text))
# Process using ngrams
ngrammed_lines, notgrammed_lines = [], []
for linenum in range(bf.num_lines):
print("Line ", linenum)
line_bantries = bf.get_line_bantires(linenum)
gramgraph = GramGraph(line_bantries)
gramgraph.process_tree()
notgrammed_lines.append(gramgraph.get_best_apriori_str())
ngrammed_lines.append(gramgraph.get_best_str())
nogram_out = post_process("\n".join(notgrammed_lines))
with open(nogram_out_fname, 'w', encoding='utf-8') as out_file:
out_file.write(nogram_out)
ngram_out = post_process("\n".join(ngrammed_lines))
with open(ngram_out_fname, 'w', encoding='utf-8') as out_file:
out_file.write(ngram_out)
print("Input : ", box_fname)
print("As is output : ", asis_fname)
print("Without ngram : ", nogram_out_fname)
print("With ngram : ", ngram_out_fname)
print("Log : ", log_fname)
}.get(sys.argv[-1][:2].lower(), logging.INFO)
replace = lambda s: banti_fname.replace('.box', s)
log_fname = replace('.{}.log'.format(logging._levelToName[loglevel]).lower())
asis_fname = replace('.ml.txt')
nogram_out_fname = replace('.nogram.txt')
ngram_out_fname = replace('.gram.txt')
logging.basicConfig(filename=log_fname, level=loglevel, filemode="w")
############################## Set-up scaler, classifier, ngram etc.
Bantry.scaler = ScalerFactory(scaler_fname)
Bantry.classifier = Classifier(nnet_fname, labels_fname, logbase=1)
ng = Ngram(ngram_fname)
Bantry.ngram = ng
GramGraph.set_ngram(ng)
############################## Read Bantries & get Most likely output
bf = BantryFile(banti_fname)
with open(asis_fname, 'w', encoding='utf-8') as f:
f.write(post_process(bf.text))
############################## Process using ngrams
ngrammed_lines, notgrammed_lines = [], []
for linenum in range(bf.num_lines):
print("Line ", linenum)
line_bantries = bf.get_line_bantires(linenum)
gramgraph = GramGraph(line_bantries)
gramgraph.process_tree()
notgrammed_lines.append(gramgraph.get_best_apriori_str())
