diff --git a/egs/cifar/v1/image/get_allowed_lengths.py b/egs/cifar/v1/image/get_allowed_lengths.py
new file mode 100755
index 00000000000..02321fdd2df
--- /dev/null
+++ b/egs/cifar/v1/image/get_allowed_lengths.py
@@ -0,0 +1,143 @@
+#!/usr/bin/env python3
+
+# Copyright     2017  Hossein Hadian
+# Apache 2.0
+
+
+""" This script finds a set of allowed lengths for a given OCR/HWR data dir.
+    The allowed lengths are spaced by a factor (like 10%) and are written
+    in an output file named "allowed_lengths.txt" in the output data dir. This
+    file is later used by make_features.py to pad each image sufficiently so that
+    they all have an allowed length. This is intended for end2end chain training.
+"""
+
+import argparse
+import os
+import sys
+import copy
+import math
+import logging
+
+sys.path.insert(0, 'steps')
+import libs.common as common_lib
+
+logger = logging.getLogger('libs')
+logger.setLevel(logging.INFO)
+handler = logging.StreamHandler()
+handler.setLevel(logging.INFO)
+formatter = logging.Formatter("%(asctime)s [%(pathname)s:%(lineno)s - "
+                              "%(funcName)s - %(levelname)s ] %(message)s")
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+def get_args():
+    parser = argparse.ArgumentParser(description="""This script finds a set of
+                                   allowed lengths for a given OCR/HWR data dir.
+                                   Intended for chain training.""")
+    parser.add_argument('factor', type=float, default=12,
+                        help='Spacing (in percentage) between allowed lengths.')
+    parser.add_argument('srcdir', type=str,
+                        help='path to source data dir')
+    parser.add_argument('--coverage-factor', type=float, default=0.05,
+                        help="""Percentage of durations not covered from each
+                             side of duration histogram.""")
+    parser.add_argument('--frame-subsampling-factor', type=int, default=3,
+                        help="""Chain frame subsampling factor.
+                             See steps/nnet3/chain/train.py""")
+
+    args = parser.parse_args()
+    return args
+
+
+def read_kaldi_mapfile(path):
+    """ Read any Kaldi mapping file - like text, .scp files, etc.
+    """
+
+    m = {}
+    with open(path, 'r', encoding='latin-1') as f:
+        for line in f:
+            line = line.strip()
+            sp_pos = line.find(' ')
+            key = line[:sp_pos]
+            val = line[sp_pos+1:]
+            m[key] = val
+    return m
+
+def find_duration_range(img2len, coverage_factor):
+    """Given a list of utterances, find the start and end duration to cover
+
+     If we try to cover
+     all durations which occur in the training set, the number of
+     allowed lengths could become very large.
+
+     Returns
+     -------
+     start_dur: int
+     end_dur: int
+    """
+    durs = []
+    for im, imlen in img2len.items():
+        durs.append(int(imlen))
+    durs.sort()
+    to_ignore_dur = 0
+    tot_dur = sum(durs)
+    for d in durs:
+        to_ignore_dur += d
+        if to_ignore_dur * 100.0 / tot_dur > coverage_factor:
+            start_dur = d
+            break
+    to_ignore_dur = 0
+    for d in reversed(durs):
+        to_ignore_dur += d
+        if to_ignore_dur * 100.0 / tot_dur > coverage_factor:
+            end_dur = d
+            break
+    if start_dur < 30:
+        start_dur = 30  # a hard limit to avoid too many allowed lengths --not critical
+    return start_dur, end_dur
+
+
+def find_allowed_durations(start_len, end_len, args):
+    """Given the start and end duration, find a set of
+       allowed durations spaced by args.factor%. Also write
+       out the list of allowed durations and the corresponding
+       allowed lengths (in frames) on disk.
+
+     Returns
+     -------
+     allowed_durations: list of allowed durations (in seconds)
+    """
+
+    allowed_lengths = []
+    length = start_len
+    with open(os.path.join(args.srcdir, 'allowed_lengths.txt'), 'w', encoding='latin-1') as fp:
+        while length < end_len:
+            if length % args.frame_subsampling_factor != 0:
+                length = (args.frame_subsampling_factor *
+                          (length // args.frame_subsampling_factor))
+            allowed_lengths.append(length)
+            fp.write("{}\n".format(int(length)))
+            length *= args.factor
+    return allowed_lengths
+
+
+
+def main():
+    args = get_args()
+    args.factor = 1.0 + args.factor / 100.0
+
+    image2length = read_kaldi_mapfile(os.path.join(args.srcdir, 'image2num_frames'))
+
+    start_dur, end_dur = find_duration_range(image2length, args.coverage_factor)
+    logger.info("Lengths in the range [{},{}] will be covered. "
+                "Coverage rate: {}%".format(start_dur, end_dur,
+                                      100.0 - args.coverage_factor * 2))
+    logger.info("There will be {} unique allowed lengths "
+                "for the images.".format(int(math.log(end_dur / start_dur) /
+                                             math.log(args.factor))))
+
+    allowed_durations = find_allowed_durations(start_dur, end_dur, args)
+
+
+if __name__ == '__main__':
+      main()
diff --git a/egs/cifar/v1/image/get_image2num_frames.py b/egs/cifar/v1/image/get_image2num_frames.py
new file mode 100755
index 00000000000..3c003bb9947
--- /dev/null
+++ b/egs/cifar/v1/image/get_image2num_frames.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+# Copyright      2018  Hossein Hadian
+
+
+""" This script computes the image lengths (with padding) in an image data dir.
+    The output is written to 'image2num_frames' in the given data dir. This
+    file is later used by image/get_allowed_lengths.py to find a set of allowed lengths
+    for the data dir. The output format is similar to utt2num_frames
+
+"""
+
+import argparse
+import os
+import sys
+import numpy as np
+from scipy import misc
+
+parser = argparse.ArgumentParser(description="""Computes the image lengths (i.e. width) in an image data dir
+                                                and writes them (by default) to image2num_frames.""")
+parser.add_argument('dir', type=str,
+                    help='Source data directory (containing images.scp)')
+parser.add_argument('--out-ark', type=str, default=None,
+                    help='Where to write the output image-to-num_frames info. '
+                    'Default: "dir"/image2num_frames')
+parser.add_argument('--feat-dim', type=int, default=40,
+                    help='Size to scale the height of all images')
+parser.add_argument('--padding', type=int, default=5,
+                    help='Number of white pixels to pad on the left'
+                    'and right side of the image.')
+args = parser.parse_args()
+
+
+def get_scaled_image_length(im):
+    scale_size = args.feat_dim
+    sx = im.shape[1]
+    sy = im.shape[0]
+    scale = (1.0 * scale_size) / sy
+    nx = int(scale * sx)
+    return nx
+
+### main ###
+data_list_path = os.path.join(args.dir,'images.scp')
+
+if not args.out_ark:
+    args.out_ark = os.path.join(args.dir,'image2num_frames')
+if args.out_ark == '-':
+    out_fh = sys.stdout
+else:
+    out_fh = open(args.out_ark, 'w', encoding='latin-1')
+
+with open(data_list_path) as f:
+    for line in f:
+        line = line.strip()
+        line_vect = line.split(' ')
+        image_id = line_vect[0]
+        image_path = line_vect[1]
+        im = misc.imread(image_path)
+        im_len = get_scaled_image_length(im) + (args.padding * 2)
+        print('{} {}'.format(image_id, im_len), file=out_fh)
+
+out_fh.close()
diff --git a/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh b/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh
new file mode 100755
index 00000000000..6ff76490303
--- /dev/null
+++ b/egs/iam/v1/local/chain/run_cnn_chainali_1c.sh
@@ -0,0 +1,246 @@
+#!/bin/bash
+
+# chainali_1c is as chainali_1b except it uses l2-regularize
+# local/chain/compare_wer.sh exp/chain/cnn_chainali_1b exp/chain/cnn_chainali_1c
+# System                      cnn_chainali_1b cnn_chainali_1c
+# WER                             14.67     12.84
+# CER                              7.31      6.40
+# Final train prob               0.0042   -0.0120
+# Final valid prob              -0.0256   -0.0199
+# Final train prob (xent)       -0.6282   -0.9973
+# Final valid prob (xent)       -0.9096   -1.1537
+# Parameters                      3.96M     3.96M
+
+# steps/info/chain_dir_info.pl exp/chain/cnn_chainali_1c
+# exp/chain/cnn_chainali_1c: num-iters=21 nj=2..4 num-params=4.0M dim=40->369 combine=-0.007->-0.007 (over 1) xent:train/valid[13,20,final]=(-1.44,-1.05,-0.997/-1.53,-1.19,-1.15) logprob:train/valid[13,20,final]=(-0.056,-0.020,-0.012/-0.056,-0.025,-0.020)
+
+set -e -o pipefail
+
+stage=0
+
+nj=30
+train_set=train
+gmm=tri3        # this is the source gmm-dir that we'll use for alignments; it
+                # should have alignments for the specified training data.
+nnet3_affix=    # affix for exp dirs, e.g. it was _cleaned in tedlium.
+affix=_1c  #affix for TDNN+LSTM directory e.g. "1a" or "1b", in case we change the configuration.
+ali=tri3_ali
+chain_model_dir=exp/chain${nnet3_affix}/cnn${affix}
+common_egs_dir=
+reporting_email=
+
+# chain options
+train_stage=-10
+xent_regularize=0.1
+frame_subsampling_factor=4
+alignment_subsampling_factor=1
+# training chunk-options
+chunk_width=340,300,200,100
+num_leaves=500
+# we don't need extra left/right context for TDNN systems.
+chunk_left_context=0
+chunk_right_context=0
+tdnn_dim=450
+# training options
+srand=0
+remove_egs=false
+lang_test=lang_unk
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+gmm_dir=exp/${gmm}
+ali_dir=exp/${ali}
+lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats_chain
+gmm_lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_lats
+dir=exp/chain${nnet3_affix}/cnn_chainali${affix}
+train_data_dir=data/${train_set}
+tree_dir=exp/chain${nnet3_affix}/tree_chain
+
+# the 'lang' directory is created by this script.
+# If you create such a directory with a non-standard topology
+# you should probably name it differently.
+lang=data/lang_chain
+for f in $train_data_dir/feats.scp \
+    $train_data_dir/feats.scp $gmm_dir/final.mdl \
+    $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
+  [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
+done
+
+
+if [ $stage -le 1 ]; then
+  echo "$0: creating lang directory $lang with chain-type topology"
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  if [ -d $lang ]; then
+    if [ $lang/L.fst -nt data/$lang_test/L.fst ]; then
+      echo "$0: $lang already exists, not overwriting it; continuing"
+    else
+      echo "$0: $lang already exists and seems to be older than data/lang..."
+      echo " ... not sure what to do.  Exiting."
+      exit 1;
+    fi
+  else
+    cp -r data/$lang_test $lang
+    silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+    nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+    # Use our special topology... note that later on may have to tune this
+    # topology.
+    steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+  fi
+fi
+
+if [ $stage -le 2 ]; then
+  # Get the alignments as lattices (gives the chain training more freedom).
+  # use the same num-jobs as the alignments
+  steps/nnet3/align_lats.sh --nj $nj --cmd "$cmd" \
+                            --scale-opts '--transition-scale=1.0 --self-loop-scale=1.0' \
+                            ${train_data_dir} data/$lang_test $chain_model_dir $lat_dir
+  cp $gmm_lat_dir/splice_opts $lat_dir/splice_opts
+fi
+
+if [ $stage -le 3 ]; then
+  # Build a tree using our new topology.  We know we have alignments for the
+  # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
+  # those.  The num-leaves is always somewhat less than the num-leaves from
+  # the GMM baseline.
+   if [ -f $tree_dir/final.mdl ]; then
+     echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
+     exit 1;
+  fi
+  steps/nnet3/chain/build_tree.sh \
+    --frame-subsampling-factor $frame_subsampling_factor \
+    --context-opts "--context-width=2 --central-position=1" \
+    --cmd "$cmd" $num_leaves ${train_data_dir} \
+    $lang $ali_dir $tree_dir
+fi
+
+
+if [ $stage -le 4 ]; then
+  mkdir -p $dir
+  echo "$0: creating neural net configs using the xconfig parser";
+
+  num_targets=$(tree-info $tree_dir/tree | grep num-pdfs | awk '{print $2}')
+  learning_rate_factor=$(echo "print 0.5/$xent_regularize" | python)
+  opts="l2-regularize=0.075"
+  opts_2="l2-regularize=0.075"
+  opts_3="l2-regularize=0.1"
+  common1="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+  common3="$opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=40 name=input
+
+  conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
+  conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
+  conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
+  conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
+  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $opts_2
+  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
+  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $opts_2
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $opts_3
+
+  # adding the layers for xent branch
+  # This block prints the configs for a separate output that will be
+  # trained with a cross-entropy objective in the 'chain' mod?els... this
+  # has the effect of regularizing the hidden parts of the model.  we use
+  # 0.5 / args.xent_regularize as the learning rate factor- the factor of
+  # 0.5 / args.xent_regularize is suitable as it means the xent
+  # final-layer learns at a rate independent of the regularization
+  # constant; and the 0.5 was tuned so as to make the relative progress
+  # similar in the xent and regular final layers.
+  relu-batchnorm-layer name=prefinal-xent input=tdnn3 dim=$tdnn_dim target-rms=0.5 $opts_2
+  output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5 $opts_3
+EOF
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
+fi
+
+
+if [ $stage -le 5 ]; then
+  if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+    utils/create_split_dir.pl \
+     /export/b0{3,4,5,6}/$USER/kaldi-data/egs/iam-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+  fi
+
+  steps/nnet3/chain/train.py --stage=$train_stage \
+    --cmd="$cmd" \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --chain.xent-regularize $xent_regularize \
+    --chain.leaky-hmm-coefficient=0.1 \
+    --chain.l2-regularize=0.00005 \
+    --chain.apply-deriv-weights=false \
+    --chain.lm-opts="--num-extra-lm-states=500" \
+    --chain.frame-subsampling-factor=$frame_subsampling_factor \
+    --chain.alignment-subsampling-factor=$alignment_subsampling_factor \
+    --trainer.srand=$srand \
+    --trainer.max-param-change=2.0 \
+    --trainer.num-epochs=4 \
+    --trainer.frames-per-iter=1000000 \
+    --trainer.optimization.num-jobs-initial=2 \
+    --trainer.optimization.num-jobs-final=4 \
+    --trainer.optimization.initial-effective-lrate=0.001 \
+    --trainer.optimization.final-effective-lrate=0.0001 \
+    --trainer.optimization.shrink-value=1.0 \
+    --trainer.num-chunk-per-minibatch=64,32 \
+    --trainer.optimization.momentum=0.0 \
+    --egs.chunk-width=$chunk_width \
+    --egs.chunk-left-context=$chunk_left_context \
+    --egs.chunk-right-context=$chunk_right_context \
+    --egs.chunk-left-context-initial=0 \
+    --egs.chunk-right-context-final=0 \
+    --egs.dir="$common_egs_dir" \
+    --egs.opts="--frames-overlap-per-eg 0" \
+    --cleanup.remove-egs=$remove_egs \
+    --use-gpu=true \
+    --reporting.email="$reporting_email" \
+    --feat-dir=$train_data_dir \
+    --tree-dir=$tree_dir \
+    --lat-dir=$lat_dir \
+    --dir=$dir  || exit 1;
+fi
+
+if [ $stage -le 6 ]; then
+  # The reason we are using data/lang here, instead of $lang, is just to
+  # emphasize that it's not actually important to give mkgraph.sh the
+  # lang directory with the matched topology (since it gets the
+  # topology file from the model).  So you could give it a different
+  # lang directory, one that contained a wordlist and LM of your choice,
+  # as long as phones.txt was compatible.
+
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/$lang_test \
+    $dir $dir/graph || exit 1;
+fi
+
+if [ $stage -le 7 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+    --extra-left-context $chunk_left_context \
+    --extra-right-context $chunk_right_context \
+    --extra-left-context-initial 0 \
+    --extra-right-context-final 0 \
+    --frames-per-chunk $frames_per_chunk \
+    --nj $nj --cmd "$cmd" \
+    $dir/graph data/test $dir/decode_test || exit 1;
+fi
diff --git a/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh b/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh
new file mode 100755
index 00000000000..65eeedcc75b
--- /dev/null
+++ b/egs/iam/v1/local/chain/run_flatstart_cnn1a.sh
@@ -0,0 +1,165 @@
+#!/bin/bash
+# Copyright    2017  Hossein Hadian
+
+# This script does end2end chain training (i.e. from scratch)
+
+# local/chain/compare_wer.sh exp/chain/cnn_1a exp/chain/cnn_chainali_1c exp/chain/e2e_cnn_1a
+# System                         cnn_1a cnn_chainali_1c e2e_cnn_1a
+# WER                             18.58     12.84     15.46
+# CER                             10.17      6.40      7.21
+# Final train prob              -0.0122   -0.0120   -0.0426
+# Final valid prob              -0.0999   -0.0199   -0.0724
+# Final train prob (xent)       -0.5652   -0.9973
+# Final valid prob (xent)       -0.9758   -1.1537
+# Parameters                      4.36M     3.96M     9.13M
+
+# steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a/
+# exp/chain/e2e_cnn_1a/: num-iters=21 nj=2..4 num-params=9.1M dim=40->12640 combine=-0.040->-0.040 (over 1) logprob:train/valid[13,20,final]=(-0.065,-0.046,-0.043/-0.081,-0.073,-0.072)
+
+set -e
+
+# configs for 'chain'
+stage=0
+train_stage=-10
+get_egs_stage=-10
+affix=1a
+
+# training options
+tdnn_dim=450
+num_epochs=4
+num_jobs_initial=2
+num_jobs_final=4
+minibatch_size=150=100,64/300=50,32/600=25,16/1200=16,8
+common_egs_dir=
+l2_regularize=0.00005
+frames_per_iter=1000000
+cmvn_opts="--norm-means=true --norm-vars=true"
+train_set=train_e2e
+lang_test=lang_test
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. ./cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+lang=data/lang_e2e
+treedir=exp/chain/e2e_bitree  # it's actually just a trivial tree (no tree building)
+dir=exp/chain/e2e_cnn_${affix}
+
+if [ $stage -le 0 ]; then
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+  # Use our special topology... note that later on may have to tune this
+  # topology.
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 1 ]; then
+  steps/nnet3/chain/e2e/prepare_e2e.sh --nj 30 --cmd "$cmd" \
+                                       --shared-phones true \
+                                       --type biphone \
+                                       data/$train_set $lang $treedir
+  cp exp/chain/e2e_base/phone_lm.fst $treedir/
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: creating neural net configs using the xconfig parser";
+  num_targets=$(tree-info $treedir/tree | grep num-pdfs | awk '{print $2}')
+
+  opts="l2-regularize=0.075"
+  opts_2="l2-regularize=0.075"
+  opts_3="l2-regularize=0.1"
+  common1="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=36"
+  common2="$opts required-time-offsets= height-offsets=-2,-1,0,1,2 num-filters-out=70"
+  common3="$opts required-time-offsets= height-offsets=-1,0,1 num-filters-out=70"
+  mkdir -p $dir/configs
+  cat <<EOF > $dir/configs/network.xconfig
+  input dim=40 name=input
+
+  conv-relu-batchnorm-layer name=cnn1 height-in=40 height-out=40 time-offsets=-3,-2,-1,0,1,2,3 $common1
+  conv-relu-batchnorm-layer name=cnn2 height-in=40 height-out=20 time-offsets=-2,-1,0,1,2 $common1 height-subsample-out=2
+  conv-relu-batchnorm-layer name=cnn3 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-layer name=cnn4 height-in=20 height-out=20 time-offsets=-4,-2,0,2,4 $common2
+  conv-relu-batchnorm-layer name=cnn5 height-in=20 height-out=10 time-offsets=-4,-2,0,2,4 $common2 height-subsample-out=2
+  conv-relu-batchnorm-layer name=cnn6 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
+  conv-relu-batchnorm-layer name=cnn7 height-in=10 height-out=10 time-offsets=-1,0,1 $common3
+  relu-batchnorm-layer name=tdnn1 input=Append(-4,-2,0,2,4) dim=$tdnn_dim $opts_2
+  relu-batchnorm-layer name=tdnn2 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
+  relu-batchnorm-layer name=tdnn3 input=Append(-4,0,4) dim=$tdnn_dim $opts_2
+
+  ## adding the layers for chain branch
+  relu-batchnorm-layer name=prefinal-chain dim=$tdnn_dim target-rms=0.5 $opts_2
+  output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5 $opts_3
+EOF
+
+  steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs
+fi
+
+if [ $stage -le 3 ]; then
+  # no need to store the egs in a shared storage because we always
+  # remove them. Anyway, it takes only 5 minutes to generate them.
+
+  steps/nnet3/chain/e2e/train_e2e.py --stage $train_stage \
+    --cmd "$cmd" \
+    --feat.cmvn-opts "$cmvn_opts" \
+    --chain.leaky-hmm-coefficient 0.1 \
+    --chain.l2-regularize $l2_regularize \
+    --chain.apply-deriv-weights false \
+    --egs.dir "$common_egs_dir" \
+    --egs.stage $get_egs_stage \
+    --egs.opts "--num_egs_diagnostic 100 --num_utts_subset 400" \
+    --chain.frame-subsampling-factor 4 \
+    --chain.alignment-subsampling-factor 4 \
+    --trainer.num-chunk-per-minibatch $minibatch_size \
+    --trainer.frames-per-iter $frames_per_iter \
+    --trainer.num-epochs $num_epochs \
+    --trainer.optimization.momentum 0 \
+    --trainer.optimization.num-jobs-initial $num_jobs_initial \
+    --trainer.optimization.num-jobs-final $num_jobs_final \
+    --trainer.optimization.initial-effective-lrate 0.001 \
+    --trainer.optimization.final-effective-lrate 0.0001 \
+    --trainer.optimization.shrink-value 1.0 \
+    --trainer.max-param-change 2.0 \
+    --cleanup.remove-egs true \
+    --feat-dir data/${train_set} \
+    --tree-dir $treedir \
+    --dir $dir  || exit 1;
+fi
+
+if [ $stage -le 4 ]; then
+  # The reason we are using data/lang here, instead of $lang, is just to
+  # emphasize that it's not actually important to give mkgraph.sh the
+  # lang directory with the matched topology (since it gets the
+  # topology file from the model).  So you could give it a different
+  # lang directory, one that contained a wordlist and LM of your choice,
+  # as long as phones.txt was compatible.
+
+  utils/mkgraph.sh \
+    --self-loop-scale 1.0 data/$lang_test \
+    $dir $dir/graph || exit 1;
+fi
+
+if [ $stage -le 5 ]; then
+  frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
+  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+    --nj 30 --cmd "$cmd" \
+    $dir/graph data/test $dir/decode_test || exit 1;
+fi
+
+echo "Done. Date: $(date). Results:"
+local/chain/compare_wer.sh $dir
diff --git a/egs/iam/v1/local/make_features.py b/egs/iam/v1/local/make_features.py
index b998464953f..8cfca5ee830 100755
--- a/egs/iam/v1/local/make_features.py
+++ b/egs/iam/v1/local/make_features.py
@@ -2,6 +2,7 @@
 
 # Copyright      2017  Chun Chieh Chang
 #                2017  Ashish Arora
+#                2018  Hossein Hadian
 
 """ This script converts images to Kaldi-format feature matrices. The input to
     this script is the path to a data directory, e.g. "data/train". This script
@@ -9,6 +10,10 @@
     (by default) as Kaldi-formatted matrices (in text form). It also scales the
     images so they have the same height (via --feat-dim). It can optionally pad
     the images (on left/right sides) with white pixels.
+    If an 'image2num_frames' file is found in the data dir, it will be used
+    to enforce the images to have the specified length in that file by padding
+    white pixels (the --padding option will be ignored in this case). This relates
+    to end2end chain training.
 
     eg. local/make_features.py data/train --feat-dim 40
 """
@@ -30,6 +35,8 @@
 parser.add_argument('--padding', type=int, default=5,
                     help='Number of white pixels to pad on the left'
                     'and right side of the image.')
+
+
 args = parser.parse_args()
 
 
@@ -49,7 +56,7 @@ def write_kaldi_matrix(file_handle, matrix, key):
             file_handle.write("\n")
     file_handle.write(" ]\n")
 
-def get_scaled_image(im):
+def get_scaled_image(im, allowed_lengths = None):
     scale_size = args.feat_dim
     sx = im.shape[1]
     sy = im.shape[0]
@@ -57,22 +64,48 @@ def get_scaled_image(im):
     nx = int(scale_size)
     ny = int(scale * sx)
     im = misc.imresize(im, (nx, ny))
-    padding_x = args.padding
-    padding_y = im.shape[0]
-    im_pad = np.concatenate((255 * np.ones((padding_y, padding_x),
+    if allowed_lengths is None:
+        left_padding = right_padding = args.padding
+    else:  # Find an allowed length for the image
+        imlen = im.shape[1]
+        allowed_len = 0
+        for l in allowed_lengths:
+            if l > imlen:
+                allowed_len = l
+                break
+        if allowed_len == 0:
+            #  No allowed length was found for the image (the image is too long)
+            return None
+        padding = allowed_len - imlen
+        left_padding = padding // 2
+        right_padding = padding - left_padding
+    dim_y = im.shape[0]
+    im_pad = np.concatenate((255 * np.ones((dim_y, left_padding),
                                            dtype=int), im), axis=1)
-    im_pad1 = np.concatenate((im_pad, 255 * np.ones((padding_y, padding_x),
+    im_pad1 = np.concatenate((im_pad, 255 * np.ones((dim_y, right_padding),
                                                     dtype=int)), axis=1)
     return im_pad1
 
 ### main ###
-data_list_path = os.path.join(args.dir,'images.scp')
+data_list_path = os.path.join(args.dir, 'images.scp')
 
 if args.out_ark == '-':
     out_fh = sys.stdout
 else:
     out_fh = open(args.out_ark,'wb')
 
+allowed_lengths = None
+if os.path.isfile(os.path.join(args.dir, 'allowed_lengths.txt')):
+    print("Found 'allowed_lengths.txt' file...", file=sys.stderr)
+    allowed_lengths = []
+    with open(os.path.join(args.dir,'allowed_lengths.txt')) as f:
+        for line in f:
+            allowed_lengths.append(int(line.strip()))
+    print("Read {} allowed lengths and will apply them to the "
+          "features.".format(len(allowed_lengths)), file=sys.stderr)
+
+num_fail = 0
+num_ok = 0
 with open(data_list_path) as f:
     for line in f:
         line = line.strip()
@@ -80,8 +113,15 @@ def get_scaled_image(im):
         image_id = line_vect[0]
         image_path = line_vect[1]
         im = misc.imread(image_path)
-        im_scale = get_scaled_image(im)
+        im_scaled = get_scaled_image(im, allowed_lengths)
 
-        data = np.transpose(im_scale, (1, 0))
+        if im_scaled is None:
+            num_fail += 1
+            continue
+        data = np.transpose(im_scaled, (1, 0))
         data = np.divide(data, 255.0)
+        num_ok += 1
         write_kaldi_matrix(out_fh, data, image_id)
+
+print('Generated features for {} images. Failed for {} (iamge too '
+      'long).'.format(num_ok, num_fail))
diff --git a/egs/iam/v1/run_end2end.sh b/egs/iam/v1/run_end2end.sh
new file mode 100755
index 00000000000..d479bfa2a73
--- /dev/null
+++ b/egs/iam/v1/run_end2end.sh
@@ -0,0 +1,76 @@
+#!/bin/bash
+# Copyright 2017    Hossein Hadian
+
+set -e
+stage=0
+nj=20
+username=
+password=
+# iam_database points to the database path on the JHU grid. If you have not
+# already downloaded the database you can set it to a local directory
+# like "data/download" and follow the instructions
+# in "local/prepare_data.sh" to download the database:
+iam_database=/export/corpora5/handwriting_ocr/IAM
+
+. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
+           ## This relates to the queue.
+. ./path.sh
+. ./utils/parse_options.sh  # e.g. this parses the above options
+                            # if supplied.
+
+
+./local/check_tools.sh
+
+if [ $stage -le 0 ]; then
+  echo "$0: Preparing data..."
+  local/prepare_data.sh --download-dir "$iam_database" \
+    --username "$username" --password "$password"
+fi
+mkdir -p data/{train,test}/data
+
+if [ $stage -le 1 ]; then
+  get_image2num_frames.py data/train  # This will be needed for the next command
+  # The next command creates a "allowed_lengths.txt" file in data/train
+  # which will be used by local/make_features.py to enforce the images to
+  # have allowed lengths. The allowed lengths will be spaced by 10% difference in length.
+  image/get_allowed_lengths.py --frame-subsampling-factor 4 10 data/train
+  echo "$0: Preparing the test and train feature files..."
+  for dataset in train test; do
+    local/make_features.py data/$dataset --feat-dim 40 | \
+      copy-feats --compress=true --compression-method=7 \
+                 ark:- ark,scp:data/$dataset/data/images.ark,data/$dataset/feats.scp
+    steps/compute_cmvn_stats.sh data/$dataset
+  done
+  utils/fix_data_dir.sh data/train
+fi
+
+if [ $stage -le 2 ]; then
+  echo "$0: Preparing dictionary and lang..."
+  local/prepare_dict.sh
+  utils/prepare_lang.sh --sil-prob 0.95 \
+                        data/local/dict "<unk>" data/lang/temp data/lang
+fi
+
+if [ $stage -le 3 ]; then
+  echo "$0: Estimating a language model for decoding..."
+  local/train_lm.sh
+  utils/format_lm.sh data/lang data/local/local_lm/data/arpa/3gram_big.arpa.gz \
+                     data/local/dict/lexicon.txt data/lang_test
+fi
+
+
+if [ $stage -le 4 ]; then
+  echo "$0: estimating phone language model for the denominator graph"
+  mkdir -p exp/chain/e2e_base/log
+  $cmd exp/chain/e2e_base/log/make_phone_lm.log \
+  cat data/train/text \| \
+    steps/nnet3/chain/e2e/text_to_phones.py data/lang \| \
+    utils/sym2int.pl -f 2- data/lang/phones.txt \| \
+    chain-est-phone-lm --num-extra-lm-states=1000 \
+                       ark:- exp/chain/e2e_base/phone_lm.fst
+fi
+
+if [ $stage -le 5 ]; then
+  echo "$0: calling the flat-start chain recipe..."
+  local/chain/run_flatstart_cnn1a.sh
+fi